The deviation of the ratio of reactants from stoichiometric ratio benefits the production of graphene with higher concentration of defects. It is indicated that the temperature of the heat treatment has remarkable influences on the structure of graphene, Raman-sensitive defects can be recovered partly by heat treatment while IR-sensitive defects are closely related with the oxidation and decomposition of the oxygen-containing groups at elevated temperature. This work indicates that SHS is a promising method to produce graphene with special magnetism, and the heat treatment is an effective way to further adjust the magnetism of graphene. This work sheds light on the study to develop carbon materials with controlled ferromagnetism.Magnetism in graphene has stimulated extensive studies to search for novel metal-free magnetic device. In this paper, we use a synthesis method far from equilibrium state named self-propagating high temperature synthesis (SHS) to produce few-layer graphene with different defect contents and then use a heat treatment process  to further control the defects in graphene. We find that the type and content of defects in graphene can be controlled by adjusting the mole ratio of reactants (Mg: CaCO Compared with other properties, magnetism in graphene8 has stimulated extensive studies to search for novel metal-free magnetic device. The emergence of magnetism in versatile natured graphene and the ability to control its properties can lead graphene to be an excellent material for spintronics and other memory based device applications which promise information storing, processing and communicating at faster speed with lower energy consumption. Research on the origin of magnetism in graphene oxide9, graphene nanoflakes12, hydrogenated graphene13 and graphene nanoribbons14 suggested that the magnetic behavior of graphene based materials is to a large part governed by their structures. Although the mechanism of graphene magnetism is complicated, extensive theoretical and experimental studies indicated that defects15, disordering4, covalent-adsorption16 and magnetic edge state in graphene nanoribbons17 and partially hydrogenated epitaxial graphene13 are the potential carriers for the magnetism in graphene.Graphene has generated a lot of activity in the area of material science due to its exceptional electronic and mechanical properties13. Recently, we have developed a facile and cost-effective method named as self-propagating high temperature synthesis (SHS) to produce few-layer graphene18. The SHS process utilizes the heat generated by the exothermic reaction of Mg and CaCO3 to sustain itself in the form of a combustion wave after external ignition. The process is of high reaction temperature, fast heating and cooling speed and far from equilibrium state, so the defect in graphene made by this method is special. We have found that few-layer graphene samples both non-doped and doped with nitrogen produced by SHS method exhibit ferromagnetic properties and have high Curie temperatures (>600 K), and the saturation magnetization and coercive field increase with the increasing of nitrogen contents in the samples19. Taking advantage of the far-from-equilibrium-state SHS process, people are expected to produce graphene with different kinds and contents of defects, which helps further clarify the relationship between defects and the ferromagnetic properties of graphene. However, few works have been done on these issues.Ferromagnetism has also been observed in graphene materials prepared by different methods like thermal exfoliation of graphitic oxide, conversion of nano diamonds, arc evaporation of graphite in hydrogen and graphene oxide partially reduced by hydrazine and further completely reduced by thermal annealing, since graphene obtained by different methods has different types and quantity of defects3) in SHS process. Secondly, in order to further improve the magnetic property of SHS graphene, we proposed a heat treatment method , which is heating the sample in vacuum environment at a certain high temperature and then cooling down to room temperature in atmospheric environment. Our works indicated that the deviation of stoichiometric ratio of the reactants under far from equilibrium state is a promising method to produce graphene with special magnetism, and that the designed heat treatment is an effective way to further adjust the ferromagnetism of graphene.In the present study, firstly, we explored the method to produce few-layer graphene with different defect concentrations by changing the ratio of reactants ; these materials were purchased from Sinopharm Chemical Reagent Co., Ltd.Here, we used the SHS method to synthesize graphene with different content of defects by changing the ratio of reactants: magnesium, (99.5% purity) and calcium carbonate  is a stoichiometric ratio according to the reaction: 2Mg + CaCO3 = 2MgO + CaO + C (graphene), while the ratio (4:1) was designed to deviate from the stoichiometric ratio. The products were expressed as M2C1 and M4C1, respectively, according to the ratios of Mg and CaCO3. 16 grams of Mg for M2C1 and 32 grams for M4C1 were added to 33.3 grams of calcium carbonate and then milled in a mortar for 20 minutes, respectively. Each sample was ignited by an electric ignition device composed by a direct current (DC) power source and a resistance-based wire heater. The ignition current was 22 A. The coarse product was placed in dilute hydrochloric acid (10 v/v %) containing ethanol (20 wt %) and sonicated for 1 h, then washed with deionized water and absolute ethanol in that order. The obtained sample was dried in a vacuum oven at 120 °C for 24 h.The SHS experiments were conducted in a stainless-steel combustion chamber under an atmosphere of carbon dioxide (99.9%)−1 and kept for 5 min in vacuum (10−4 Pa), then cooled down to room temperature within 5 minutes in air by opening the valve.Every graphene sample (M2C1 or M4C1) was divided into 4 parts and three of them were heated at 500 K, 650 K and 800 K, and named as M2C1-500 or M4C1-500, M2C1-650 or M4C1-650 and M2C1-800 or M4C1-800, respectively. As a contrast, the initial M2C1 and M4C1 sample without heat treatment was named as M2C1-G and M4C1-G (G stands for the generated graphene). The heating rate from room temperature to the desired temperature was 5 K·min−1 in air atmosphere form 300 K to 1200 K. The nitrogen adsorption/desorption measurements were carried out on Belsorp mini II (Japan) at 77 K to obtain the specific surface area of M2C1-G and M4C1-G. Before adsorption/desorption tests, the samples were degassed at 150 °C for 4 hours with vacuum pumping.The phase composition of the as-prepared powders was analyzed by powder X-ray diffraction (XRD) analyses (Philips X’Pert diffractometer) with CuKα radiation. Environmental scanning electron microscopy  and high-resolution transmission electron microscopy (HRTEM JEM-2100) were used to observe the morphology of the graphene sheets. The TEM specimens were prepared by dropping ethanol/water (38 v/v %) solution containing 1 wt % graphene onto a copper grid and drying at 100 °C. Raman spectra was obtained using a Raman Station  with a 532 nm wavelength laser corresponding to 2.34 eV. X-ray photoelectron spectroscopy  was utilized to determine the bonding characteristics of the samples. All XPS peaks were calibrated according to the C 1 s peak (284.6 eV). The magnetic properties were measured using a Quantum Design MPMS magnetometer based on a superconducting quantum interference device (SQUID). Thermogravimetric analysis (TGA) was performed on a Netzsch STA 449 F3 under a heating rate of 10 K·min2 g−1 according to the BET method. Then we can evaluate the layer of M2C1-G is about 7 through the SSA of monolayer graphene is 2630 m2 g−1. According to the above analysis and the Raman analyses in the following section, we conclude that the products synthesized by SHS method are few-layers graphene.Figure 3) for SHS reaction. The stoichiometric mole ratio of the reaction between Mg and CaCO3 is 2:1, which is just the ratio for M2C1-G, while the ratio for M4C1-G is 4:1, much higher than the stoichiometric ratio. The deviation of the stoichiometric ratio for M4C1 means that Mg is excessive for the reaction, and the excessive Mg may play multiple roles in the SHS reaction. Firstly, the excessive Mg may melt at 648 °C and volatilize at 1107 °C, which can absorb large amount of heat produced by the exothermic SHS reaction (ΔH = −632 kJ/mol) and decrease the maximum temperature of the reaction. Secondly, the gaseous Mg in the enclosed space of reaction container may affect the growth process of graphene since they may decrease the collision probability of the reactive carbon atoms produced during the SHS reaction process. As a result, we can deduce that the reaction temperature for M4C1-G is lower than that of M2C1-G which benefits the production of smaller and thinner sheets for M4C1-G as shown in Fig. The difference of the morphology between M2C1-G and M4C1-G can be understood by considering the mole ratios of reactants ; the peaks at 1141 cm−1, 1717 cm−1, 2850–2920 cm−1 and 3200–3600 cm−1 are attributed to the C-O-C, C=O, C-H and O-H vibration, respectively. On the one hand, from Fig. 2O (1624 cm−1) and O-H vibration (3200–3600 cm−1) with the increase of heat treatment temperature, suggesting the remove of hydroxyl and water on graphene; the peaks corresponding to oxygen-containing groups are not clear for M2C1-G, suggesting that M2C1-G has good chemical stability. On the other hand, it is interesting to see that the FTIR spectra of M4C1-G is quite different from that of M2C1-G. The peaks corresponding to epoxy, hydroxyl, carbonyl and carboxyl groups can be both found for M4C1-G and M4C1-800; however, the relative intensities of peaks corresponding to epoxy, carbonyl and carboxyl groups for M4C1-800 increase obviously, while the peaks corresponding to O-H vibration almost disappear, compared with those for M4C1-G. Consequently, it can be concluded that M2C1-G has less oxygen-containing groups and is more stable for the heat treatment than M4C1-G and that the oxidization of graphene happens for M4C1-G heat-treated at high temperature.Figure −1 and the result has been added in Fig. 2. From the results of DSC and TG analysis, we conclude that the SHS graphene had two types of structure. One is easily oxidized at low temperature, corresponding to the oxygen-containing groups and carbon defects; the other is more thermally stable, oxidized at higher temperature, assigning to the defect-free parts in SHS graphene20. But the ratio of the two exothermic peaks in the curves of DSC for the two samples is different. The relatively intensity of the first peak to the second peak in M4C1-G is much higher than that in M2C1-G, indicating that M4C1-G contained more oxygen-containing groups and its thermal stability is lower than that of M2C1-G, which are consistent with the results from FTIR and XPS.To better study this behavior, we performed the Thermogravimetric Analysis (TGA) and the differential scanning calorimetry (DSC) of M2C1-G and M4C1-G in air environment at the heating rate of 10 K·min21. Gaussian decomposition and Lorentz decomposition are employed in this fitting. The C1s component can be deconvolved into six components: sp2 C=C (284.4 ± 0.1 eV), sp3 C-C (285.4 ± 0.1 eV), C-O (286.4 ± 0.1 eV), C=O (287.5 ± 0.2 eV), O=C-O (288.6 ± 0.2 eV) and π-π* satellite peak (290.5 ± 0.1 eV)24. In order to obtain more detailed information, the contents of components in C 1 s of M2C1 and M4C1 treated at different temperatures are analyzed according to the fitting and the results are shown in the Fig. XPS characterizations are further performed to analyze the elemental composition and C/O configuration in the samples. The XPS survey spectra of the samples in Fig. 2 and sp3 C in graphene, the well opposite trend suggests that the oxidized sp2 carbons are mostly changed to sp3 carbons and vice versa. Similar trend can also be found in M4C1 sample.Figure XPS results in Fig. 28. The Raman spectra of the M2C1 and M4C1 samples treated at different temperatures are shown in Fig. −1 represents the in-plane bond-stretching motion of the pairs of sp2 hybridized C atoms (the E2g phonons); the D band at 1341 cm−1 corresponds to breathing mode of rings or K-point phonons of A1g symmetry; and the second-order D (2D) band at 2678 cm−1 originates from a two phonon double resonance process29. The 2D peaks of the M2C1 and M4C1 samples around 2678 cm−1,which shift greatly to lower wavenumber compared with that of graphite (2714 cm−1), can identify the samples as few-layer graphene31.Raman spectroscopy is considered to be an effective tool for characterization of mono-, few-, or multil-layer grapheneID) to the G peak (IG) in graphene is directly proportional to the level of defects in the sample33. Here, the intensity ratios (ID/IG) is used to demonstrate the relatively change of ID to IG with the heat treatment temperatures as shown in Fig. −2) of graphene can be investigated and defined as nD = 5.9 * 1014 * EL−4 * (ID/IG)−1 29, where the laser energy EL = 2.34 eV (λ = 532 nm) and the calculated defect density is shown in Fig. 34 and two orders higher than highly ordered pyrolytic graphite irradiated by 140 eV Ar+ ions reported by Ugeda35.The relative intensity of the D peak (ID/IG of the M4C1 sample is much higher than that of the M2C1 sample treated at the same temperature. This result indicates that the mole ratio of the reactants (Mg: CaCO3) plays an important role on the defect density in the SHS products, the deviation of the ratio of reactants (Mg: CaCO3) from stoichiometric one in the case of M4C1 benefits the production of graphene with higher defect density. The ratios of ID/IG of both M4C1 and M2C1 decrease with the increase of heat treatment temperature, while the decline for M4C1 is more obvious than that of M2C1. But the defect concentration in M4C1 is still higher than that in M2C1 overall even after heat-treated at 800 K. The reduction of defect density with the increase of the heat treatment temperature suggests the repair of the Raman-sensitive defects is the main process during the heat-treatment process. Finally, we can conclude that the ratio of reactants is the main factor for the formation of defects in SHS graphene and that the heat treatment can repair part of the defects characterized by Raman spectra, especially for M4C1.From Fig. I2D/IG has been used for the identification of the number of graphene layers. The value of I2D/IG obtained from Fig. I2D/IG value is about 0.6 for M2C1 and about 0.4 for M4C1, which are larger than that of graphite (about 0.3)36. These results also indicate that M2C1 and M4C1 are few layer graphene. In addition, comparing the ratios of the samples treated by different temperatures, we can find that the ratios of I2D/IG for M2C1 and M4C1 at any treated temperatures do not change significantly, which indicates that the heating treatment has no obvious effect on the number of graphene layers. At last, we summarize the full width at half-maximum (FWHM) of the 2D band in the spectra for all samples in Fig. The ratio of 37, the FWHM of the 2D band in graphene could be a quantitative guide to distinguish the layer number (single- to five-layers) of few- layer graphene. However, it is based on graphene produced by mechanical exfoliation which has less defects, and the research about the relationship between defective graphene and FWHM of 2D peak has not yet been reported. In addition, the size of laser light spot for Raman spectra we used is about 100 μm and laser light may penetrate many graphene sheets in its light path, so the 2D peak we got reflects the information of many graphene sheets, which is composed of many overlaid 2D peaks. So the FWHM of 2D peak may not provide us with exact information about the number of SHS graphene layer.As mentioned in the research of J. T. L. ThongPowder X-ray diffraction is used to analyze the phases in M2C1 and M4C1 samples as shown in Fig. Ms) and heat treatment temperatures obtained from Fig. 19, the total ferromagnetic impurities  in SHS graphene are less than 15 ppm, indicating the ferromagnetic contribution of impurities could be neglectable. Therefore, the results represent that the ferromagnetism of SHS graphene is due to the structure defects in it. For comparison, we further summarized the Ms values of several carbon materials at room temperature reported in the references as shown in Fig. To investigate the magnetic properties of the SHS samples, the magnetization behaviors versus magnetic field curves for M2C1-G and M4C1-G and the heat-treated samples by 500 K, 650 K and 800 K are measured at room temperature (300 K) in the magnetic field range from −5000 Oe to 5000 Oe, as shown in Fig. Ms changing tendencies of M2C1 and M4C1 heat-treated at different temperatures are quite different as shown in Fig. Ms for M2C1 and M4C1 are affected by the changes of the defects in them. The changing tendency of defect concentration for M2C1 in Fig. Ms for M2C1, however, it is quite different for M4C1. It may indicate that Raman-sensitive defects must not be the only representation for the ferromagnetism in graphene.Surprisingly, it can be also found that the It is well known that Raman measurement is sensitive to symmetric structures while FTIR spectra is sensitive to asymmetric structures. We divide the defects of the SHS graphene into Raman-sensitive and FTIR-sensitive defects. Edges (zigzag and armchair), vacancies  and disordering are the defects originating from the broken of the C-C bonds which make graphene sheets distorted. These defects can be measured by Raman spectrum and mentioned as Raman-sensitive. The defects corresponding to the oxygen-containing groups include carboxyl group, carbonyl group and hydroxyl group, etc., which are connected to the graphene layers by covalent bonds and also introduce various edges and defect sites. They are FTIR sensitive and mentioned as FTIR-sensitive.Ms tendency between M2C1 and M4C1 by considering the changes of both Raman- and FTIR-sensitive defects. On the one hand, the Raman-sensitive defects in graphene have been repaired in a certain extent as shown in Fig. Ms of the SHS graphene. On the other hand, the content of carboxyl group increases with the increase of heat treatment temperature, which could increase the Ms. M2C1 is more stable at elevated temperature and has less content of carboxyl and other oxygen-containing groups, so the Raman-sensitive defects play a more important role on the ferromagnetism than FTIR-sensitive one, so the Ms for M2C1 has a similar trend with the change of Raman-sensitive defects. Since M4C1 is more thermal sensitive and easy to be oxidized at higher temperature as above mentioned the effect of FTIR-sensitive defects on the ferromagnetism may overcome that of Raman-sensitive defects, as a result the Ms for M4C1 has a similar trend with the FTIR-sensitive defects. In addition, according to XPS results in Fig. Ms for M4C1 or M2C1, the carboxyl group must be the origin of the ferromagnetism for SHS graphene. Based on the analyses above-mentioned, we obtain a more comprehensive scene of the magnetism properties of the SHS graphene and the effect of heat treatment on them. Both Raman-sensitive and FTIR-sensitive defects could contribute to the ferromagnetic properties of graphene. The heat treatment plays an important role on elucidating the effect of different factors for the ferromagnetism of graphene.We could explain the difference of the 3) from stoichiometric ratio benefits the production of few-layer graphene with smaller and more plicated sheets, and also benefits the production of both Raman-sensitive and FTIR-sensitive defects. Secondly, the heat treatment method could adjust the contents and types of defects by using the competitive relationship of the repair and oxidation processes. Thirdly, there are two origins of the ferromagnetism of the SHS graphene, associated with the Raman-sensitive and IR-sensitive defects respectively. The Ms trends could be explained by considering the changes of both Raman-sensitive and FTIR-sensitive defects. As a result, this work sheds light on the study to develop carbon materials with controlled ferromagnetism.In this study, we obtain a more comprehensive scene of the ferromagnetic properties of the SHS graphene and the methods to tune them. Firstly, the deviation of the mole ratio of SHS reactants (Mg: CaCOSupplementary Information


Important characteristics of this low-temperature response are a pronounced selectivity to acid- and base-forming gases and a large disparity of response and recovery time constants which often leads to an integrator-type of gas response. We show that this kind of sensor performance is related to the trend of semiconductor gas sensors to adsorb water vapor in multi-layer form and that this ability is sensitively influenced by the surface morphology. In particular we show that surface roughness in the nanometer range enhances desorption of water from multi-layer adsorbates, enabling them to respond more swiftly to changes in the ambient humidity. Further experiments reveal that reactive gases, such as NO Gases that could be detected under such conditions are NO00 °C or alcohols ,5,6,7. Ialcohols ,12,13. Aalcohols ,17,18,19alcohols ,21,22. Talcohols ,26,27,28alcohols ,31,32,33alcohols ,38,39,40alcohols . In esseaterials . The intaterials ,44 whereaterials ,45.In the following, we shed more light on the ability of semiconductor substrates of acquiring multi-layer water adsorbates and of exhibiting an integrator-type gas response with regard to acid- and base-forming gases. Below, we will demonstrate that Brunauer–Emmett–Teller (BET) water adsorbates ,47 can r2 layers with distinctly different and well-controllable surface morphologies. Typical morphologies obtained with both methods are shown in 2 at room temperature and high-temperature annealing in air. As shown in the surface SEM of 2 grains. Almost all of these grains have a visible sub-structure with feature sizes in the range of a few nanometers only. In addition, a larger number of such smaller grains can be seen in the empty spaces between the dominating larger grains. These latter films were produced by a modified RGTO  technique, starting with a room-temperature evaporation of metallic tin and a high-temperature annealing step in air to allow the molten tin to become oxidized and to be transformed into SnO2. A more detailed description of the preparation processes and the resulting material properties is presented in In order to test the effect of the surface morphology on the room temperature gas response, we have adopted two different methods of preparing SnO2, NH3, H2, C2H4, CO, and ethyl alcohol were performed. All gases were diluted to the desired concentrations in dry synthetic air. The sensor operation temperature could be controlled with the help of a substrate heater, allowing measurements between room temperature and about 100 °C. In order to minimize parasitic condensation on the chamber walls, the walls were slightly heated (~40 °C) during all gas sensing tests. The actual humidity concentration inside the test chamber was independently controlled using a humidity sensor.In order to assess the gas sensing properties of the above layers, the films were fitted with contact pads and gas sensing tests with humidity, NO2, both in its flat-surface and nano-morphology versions, does respond to water vapor with the expected resistivity decrease. The magnitude and the dynamic behavior of the humidity responses, however, clearly depend on the surface morphology. The morphologies of the tested films are those shown in Before starting gas sensing tests, both kinds of sensing materials were heated and stored in a flow of dry synthetic air to remove any humidity that might have existed on the sensor surface. Thereafter the films were cooled to nominally room temperature and subjected to pulses of humidified synthetic air with the level of humidity increasing in each subsequent exposure pulse. Between each exposure pulse the initially dry flow of synthetic air was restored. The data displayed in 2 film with a smooth surface exhibits an almost ideal kind of integrator response with the humidity response increasing linearly during each exposure pulse and with the response staying constant in between exposure pulses. From the fact that there is no visible drop in the sensor response over periods ranging up to 40 min, it is evident that the time constants for desorption from room temperature substrates is in the order of several hours at least. Due to this extremely slow desorption, the flat-surface films accumulate increasing amounts of water as the number of humidity exposure pulses is increased. In the nano-morphology case a different behavior is observed: the humidity response also rises linearly during each exposure pulse. After each exposure pulse, however, the humidity response drops much more rapidly than in the flat-surface case. Desorption time constants in this case were in the order of one hour only.The data in 2 surfaces and that the degree of sticking depends on the surface morphology. Since besides morphology, temperature is an important parameter controlling the adsorption/desorption kinetics, these experiments were repeated at increasingly higher sensor operation temperatures, as reported in These first experiments show that adsorbed water vapor tends to stick to SnO2 and NH3 exposure steps to investigate their response towards water-soluble gases. In order to elucidate the effects of background humidity this experiment was repeated four times, each time increasing the humidity level of the synthetic air carrier gas from almost zero up to about 90% relative humidity. 2 films to these exposure sequences. For better comparison, the sensor resistances of the two versions of SnO2 have been normalized to their starting values at the beginning of these sensing tests.Both kinds of sensing materials were subjected to a series of NO2 do respond to these acid- and base-forming gases. Considering the absolute magnitude of the gas response it is seen that SnO2 layers with nano-crystal morphology exhibit a larger response than their flat-surface counterparts when the background humidity level is low. In the limit of high-humidity levels the magnitude of the gas response becomes more or less similar and independent of the surface morphology. Turning to the kinetics of the gas response, it is seen that both morphologies exhibit a more or less pronounced integrator-type response as long as the background humidity is low and a faster and more reversible response when the background humidity is high. This latter result demonstrates that the presence of water vapor does have an accelerating effect on the desorption behavior of NO2 and NH3 molecules from the adsorbed water layers, independent of the kind of surface morphology. Comparing NO2 and NH3, it is seen that the adsorption and desorption rates of NO2 are relatively high, whereas they are very low in the case of NH3. In this latter case the accelerating effect of the background humidity on the adsorption and desorption processes is much more important than in the case of NO2. In order to reveal any interaction of the dissolved NO2 and NH3 molecules inside the water adsorbate layers, control experiments were performed in which the sequence of the NO2 and NH3 exposure steps was inverted. As these latter experiments yielded similar results, it appeared that the adsorbates do not measurably react inside the adsorbed water layers and that therefore only their relative concentrations do determine the actual level of gas response.As a key result one can observe that both kinds of SnO2 layers. In addition the response to this latter group of gases also did not depend on the level of background humidity. Again, and consistent with the results in 2 response than those with a granular surface.In order to gain further insight into the gas sensing mechanisms operative on both kinds of materials, similar gas sensing tests were performed with the samples being exposed to increasing concentrations of non-acid or non-base-forming gases. These latter data, displayed in 2 layers are very similar to those observed on hydrogenated diamond samples [BETb(T) standing for the so-called BET constant: Overall, the observed gas response characteristics of the SnO samples ,38,39,40 samples  can sati samples ,47,48. I2O molecules to the adsorbent surface is stronger than the binding between water molecules in liquid water The two energy parameters in this latter equation, olecule) . A numerThis latter fact is also illustrated in The considerations in 2 surfaces are reasonably hydrophilic and that therefore BET multilayer adsorbates do indeed form. Supporting evidence into this direction is provided by the fact that the contact angle of water is small on the investigated sensor surfaces  of SnO2 powders. Electron beam evaporation onto room temperature substrates and under standard vacuum conditions yielded amorphous material with an excess amount of tin. In order to arrive at fully stoichiometric, crystalline SnO2 layers, the evaporated films were annealed in laboratory air at 600 °C for 20 h [2. Scanning electron microscopy further revealed that these films have a comparatively flat surface, are compact and do not exhibit any significant porosity. This latter fact is demonstrated in 2 layer on top. In order to obtain metallic Sn layers with a flat surface morphology, the substrates were not heated during deposition. Oxidation of the evaporated tin layers was performed afterwards by annealing at 600 °C in laboratory air. Upon annealing, the evaporated tin layers melt  .x grains as shown in x grains scaled with the thickness of the Sn precursor films. As shown in the example of x layer formed from a 40 nm Sn precursor film consisted of an irregular array of SnOx grains with an average diameter around 40 nm. In addition, one can observe a large number of very small grains interspersed in between the larger ones. Such films did not electrically conduct as the disconnected grains failed to form percolation paths that could connect the contact pads at the periphery of the SnOx samples. In order to obtain electrically conducting layers, a second precursor layer of Sn was evaporated on top of the first layer of SnOx grains and annealed in air as before. These second and third layers of SnOx grains formed bridges between the initially disconnected grains and thus allowed electrical currents to flow over macroscopic distances. A typical example of such an interconnected film is shown in Oxidation of very thin Sn layers . The resulting pH changes in the BET layers are then communicated to the sub-surface electronic system inside the MOx semiconductors, thus forming an electrically detectable output signal. A huge variety of combustible gases , which yield large signals on heated MOx gas sensors, are either completely rejected by the BET water layers  or fail to electrolytically dissociate  and thus go undetected. BET adsorbates thereby considerably contribute to gas selectivity. Concerning the kinetics of those gases that actively contribute to the sensor response, it is found that the desorption of H2O, NO2 and NH3 is much more sensitively influenced by the effects of surface morphology, ambient humidity and sensor operation temperature than their adsorption. While rough surface morphologies, high ambient humidity and elevated sensor operation temperatures enhance desorption, desorption becomes increasingly unlikely in the limit of flat surface morphologies, low ambient humidity and room-temperature operation. In this latter case MOx gas sensors tend to function as gas dosimeters. As has been shown in recent papers [The room-temperature response of SnOt papers ,32,33, a


PGOD materials are natural chemical extensions of graphene that promise to have an enhanced range of properties and applications. Experimentally realised molecules based on two PGOD nodes exhibit a bi-stable closed-shell/multi-radical character that can be understood through competing Lewis resonance forms. Here, following the same rationale, we predict that similar states should be accessible in PGOD materials, which we confirm using accurate density functional theory calculations. Although for graphene the semimetallic state is always dominant, for PGOD materials this state becomes marginally meta-stable relative to open-shell multi-radical and/or closed-shell states that are stabilised through symmetry breaking, in line with analogous molecular systems. These latter states are semiconducting, increasing the potential use of PGOD materials as highly tuneable platforms for future organic nano-electronics and spintronics.Post-graphene organic Dirac (PGOD) materials are ordered two-dimensional networks of triply bonded  The lack of band gap controllability in graphene severely restricts its use in nanoelectronics. Here, the authors predict that post-graphene organic Dirac materials should allow for exceptional electronic tunability between graphene-like semimetallicity and multi-radical and/or closed-shell semiconducting states. Organic nanosystems based on sp2 carbon networks are seen by many as an essential basis for many future technologies1, arguably provoking a paradigm shift in materials science. Graphene, the simplest two-dimensional (2D) hexagonal network of sp2 hybridised carbon, plays an especially prominent role in this respect5. Since its isolation4, researchers have become fascinated with its many exceptional properties, which are progressively being more fully understood and exploited in many fields of science and technology. One particularly appealing characteristic of graphene is the existence of Dirac cones in its electronic band structure5. This feature, due to specific bands crossing at the Fermi level, EF, gives rise to semimetallicity and ultra-high electron mobilities, and has promoted graphene as a potential candidate to replace silicon in the next generation of nanoelectronic devices5. However, the intrinsic connection of these features with the extended high symmetry of its electronic state limits its use in applications requiring nanostructuring .The triply bonded 9 and carbon-based11 2D materials ). An important class of PGOD materials are the so-called graphynes13 and graphdiynes13, which present sp2 carbon atoms linked via alkyne groups in a variety of ways14. Although some graphdiynes have been synthesised15, those predicted to be Dirac materials are still awaiting experimental realisation. Recently, a series of 2D conjugated networks were predicted to present Dirac cones in their bandstructures10. These networks present a common basic skeleton: a hexagonal array of triply bonded sp2 carbon atoms  connected by π-conjugated linkers . Herein, we further propose graphphenyl (GP), obtained when using phenyl ring as linker  or buta-1,3-diyn-1,4-diyl (-C≡C-C≡C-) alkyne linkers, respectively. Finally, graphene is the densest hexagonal array of sp2 carbon nodes directly linked by bonding based on π–π orbital overlap. This family of 2D materials is summarised in Fig. The discovery of graphene has triggered a global search for other 2D materials with similar characteristics, but with higher electronic tunability and greater resilience to nanostructuring. In this direction, ab initio computational modelling has predicted that Dirac cones should be in the band structures of a number of non-carbon-basedsee Fig. . Replaci16, one of the oldest bi-radical compounds ever synthesised. The triply bonded methyl radical carbon atoms in Tschitschibabin’s hydrocarbon bear the two unpaired electrons of the bi-radical molecule. Due to the connectivity linking these two radical sites, the molecule possesses two possible Lewis resonance forms: 1) the open-shell anti-ferromagnetic (AFM) one with two unpaired localised electrons , it is chemically reasonable to expect that these extended resonance forms should lead to the accessibility of similar electronic states in all cases.Focusing on GDP  cutting it into nanoribbons22, (iii) covalent grafting of molecules23 or atoms25, (iv) inclusion of defects26, or (v) applying external strains28. Analogously, computational modelling has predicted similar states in α-graphyne (GY) upon application of strain29 or by forming α-GY nanoribbons31. These studies lend support to our chemically motivated proposal that extended resonance forms may lead to open-shell multi-radical and/or closed-shell solutions being stabilised in all the materials depicted in Fig. As far as we are aware, none of the family of materials in Fig. 35 and that of PGOD materials. In all cases, these solutions are found to be energetically accessible and to significantly affect the electronic properties of the host materials, highlighting the potential of such systems for future organic electronics/spintronics applications.In this work, we present a systematic computational modelling study employing accurate density functional theory (DFT) based calculations to assess the existence and stability of low lying localised electronic states in all the 2D PGOD materials shown in Fig. 36, especially for capturing the most subtle electronic features displayed by many systems . The molecular computational chemistry community has a long tradition of using hybrid functionals where, especially for electronic structure, they are known to generally outperform GGA functionals41. A particular case is that of bi-radical/quinoidal compounds (such as Tschitschibabin’s hydrocarbon), which have attracted much attention in recent years for its use in organic electronics33 and organic magnetism42. When these compounds are reduced or doped by other elements , charge transfer phenomena between the two sp2 centres takes place44. The computational assessment of the electronic ground state of such molecular systems is thus quite challenging38. DFT calculations employing GGA functionals over-delocalise valence electrons, and cannot stabilise the localised states found in experimental observations. Calculations using hybrid functionals, on the other hand, are found to be able to capture the quite complex and sensitive electronic structure of such π-conjugated mixed valence compounds45.Largely due to their computational efficiency and relative accuracy, DFT based approaches have become the dominant method of choice when simulating the electronic structures of periodic materials. For numerous cases  the standard generalised gradient approximation (GGA) produces reliable results. However, hybrid functionals ) are now being widely adopted by material’s modellers46, or local density approximation (LDA)47 functionals, and seldom utilise hybrid functionals27. A reason for this might be that the high tendency of GGA to delocalise electrons ensures the success in finding the known, and highly studied, semimetallic solutions. However, knowing how critical the proportion of HFE is for obtaining the correct localised solutions in π-conjugated molecular systems45, it is reasonable to think that possible localised solutions in Dirac cone materials might have remained elusive due to this bias. In this work, we find that PBE048 is a functional providing a good balance between localisation and delocalisation . Therefore, the main discussion is based on PBE0 results. For comparison with previous works, results obtained by the PBE GGA functional49 are included in Supplementary Figs. Most of the periodic modelling studies assessing the electronic structure of Dirac materials are performed using pure GGAsp2 carbon atoms siting at the nodes of the hexagonal network, due to the inclusion of (-C≡C-)n linkers (where n = 0–2), between them. In these cases Dirac cones appear at the high-symmetry reciprocal space K point, as previously found29. Although the nodal sp2 centres in GP and GDP lay in a plane, these networks cannot be regarded as one-atom planar materials, due to the fact their phenyl rings are partially twisted by ~35o due to inter-ring steric hindrance . These materials also present a zero band gap feature at K. This feature can be understood as resulting from the high symmetry of the hexagonal lattice, which is preserved in all cases.In Fig. see Fig. , x views6 m/s for graphene to 3.04·106 m/s for GDP. These values are in line with those of previous studies50 and indicate that the increasingly separated π-conjugated sp2 carbon centres when going from graphene to GDP has only a modest effect on the ballistic transport.Overall, it is possible to see a clear reduction in the energy scale of the band structures through the series from graphene to GDP, accompanied with a corresponding flattening of the bands. As reported in  Supplementary Fig. sp2 triply bonded carbon atoms  may be possible for larger cells than used herein.[53, covalently grafting of atoms and molecules54 or the application of external strains55. Such approaches, however, tend to significantly affect other fundamental and technological interesting properties of graphene . Conversely, GP and GDP display a sizeable band gap around the Fermi level  arises from sp2 triply bonded carbon atoms, in full agreement with the open-shell character of such centres (see above).Finally, in bi-radical/quinoidal compounds it is known that a ferromagnetic (FM) alignment of unpaired electrons (a triplet state) can be induced by thermal excitationsK-vector reversal symmetry  resulting in a lifting of the degeneracy of spin-up and spin-down bands. One effect of this spin polarisation is to induce a small (~0.3 eV) energy gap at the previously spin degenerate crossing at K resulting in two crossings between bands of the same spin above and below the Fermi level. Additionally, two further crossings between bands of different spin appear at the Fermi level at K + k1 and K−k2. These two crossings close to K are reminiscent of Weyl points, however, as they are constituted by bands of different spin, they indicate the possibility of spin helical Dirac states as found on the edges of graphene nanoribbons (1D)59 and within surfaces of toplogical insulators (2D)60. Applying spin-orbit coupling (SOC) in our calculations reveals that these crossings are maintained and not gapped out  by SOC. The relative energies of these spin-polarised solutions with respect to the purely semimetallic solutions  and chemistry  which, until now, have not had significant overlap. Bi-radical/quinoidal molecules are finite systems and, consequently, their electronic character tends to vary between two localised electronic solutions: namely the bi-radical AFM and the closed-shell quinoidal, as represented by the bottom dotted line in Fig. Electronically, the behaviour of PGOD materials lies between that of bi-radical/quinoidal molecules and graphene ; here using 6 × 6 × 1 and 18 × 18 × 1 meshes for PBE and PBE0, respectively  to obtain reasonable band structures. All DFT based calculations were performed employing the all-electron FHI-AIMS code63, using a light/Tier1 numerical atom-centred orbital (NAO) basis set64. This basis set approximately provides results of a similar or higher quality to those obtained with valence triple-zeta plus polarisation Gaussian type orbitals65. For the periodic band structure calculations the energy zero (Fermi level) is set by the G = 0 component of the long-range electrostatic potential. In addition, the entire set of PBE calculations was also performed using the Vienna ab initio simulation package (VASP)66, using a plane-wave basis set with a 415 eV kinetic energy cut-off. Due to the very-high computational cost required for hybrid calculations using VASP, only single-point PBE0 calculations using the PBE0-optimised structures from FHI-AIMS were used in our comparisons. We find that in all cases the corresponding PBE and PBE0 results from VASP and FHI-AIMS are fully consistent, thus negating any significant influence of the DFT implementation  in our results.Optimizations of the atomic and electronic structures were performed using different initial electronic guesses to induce different electronic solutions . This set of optimizations was separately performed using the GGA functional: PBE45. For such systems, even widely used hybrid functionals such as PBE048 (25% HFE) or B3LYP67 (20% HFE) still tend to over-delocalise electrons due to the inherent self-interaction error in GGA68. However, use of functionals having a relatively large percentage of HFE, although useful for stabilising localised states, can prevent one finding fully delocalised metallic solutions – essential for extended materials such as graphene69. As a consequence, we find that the PBE0 functional provides a good balance between electronic localisation and delocalisation for the 2D materials we consider. We note that the optimised structures obtained at using the PBE functional  are made openly available in Supplementary Data Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1


The aim of this study was to evaluate the expression of PD-L1 in oropharyngeal squamous cell carcinoma. Its relation with clinicopathological variables, tumor infiltrating lymphocytes and survival was also determined.+ and CD8+ T cell count. A Cox proportional hazard model proved that solely infiltrating CD8+ T cells exhibit a positive effect on overall survival Positive PD-L1 status for the SP142 clone related with improved overall survival in oropharyngeal squamous cell carcinoma. Tumors heavily infiltrated by tumor infiltrating lymphocytes were also linked with better outcome, and this as well for the total number of tumor infiltrating lymphocytes as for the CD3Formalin-fixed, paraffin-embedded tissue from oropharyngeal tumors of 99 patients was immunohistochemically stained for PD-L1 (SP142 and 22C3 clones), CD3, CD8 and FoxP3. Expression of PD-L1, CD3, CD8, FoxP3 and HPV status were correlated with clinicopathological variables. Overall survival was determined by a log-rank (Mantel–Cox) test whereas the Cox proportional hazard model was used for multivariate analysis.+ T lymphocytes constitute an independent prognostic marker in patients diagnosed with oropharyngeal squamous cell carcinoma. PD-L1 positivity for SP142, but not for 22C3, also tends to have a positive effect on survival in oropharyngeal squamous cell carcinoma.Our results demonstrate that CD8 Over the last decades there has been an increase in oropharyngeal squamous cell carcinoma (OSCC) which has mainly been attributed to human papillomavirus (HPV) infection , 2. HPV-Programmed cell death protein 1 (PD-1) is a 50-55 kDa type I transmembrane receptor expressed by activated T and B cells, as well as by monocytes and dendritic cells. It has two binding partners, PD-L1 and PD-L2, each specific for various tissue types and with specific expression patterns. PD-L1 is expressed on immune cells (ICs) including T cells, dendritic cells and monocytes. In addition, PD-L1 is expressed in the context of T cell exhaustion during chronic viral infection (e.g. HPV infection) and by tumor cells (TCs) –12. The P = 0.5673). More HPV+ specimens were detected among poorly differentiated and basaloid tumors (11/32) compared to well/moderately differentiated tumors .The 99 patients included in this study were predominantly male (83%), and represented primarily stage III/IV tumors (83%). Most of the tumors originated from the tonsil (43%) and were moderately (62%) differentiated. The overall HPV prevalence rate was 19% (19/99) with no difference in distribution along all topographic sites , IHC 1 (15%), IHC 2 (12%) and IHC 3 (10%). Five samples (5%) could not be evaluated. Using the 22C3 clone, distribution of tumoral IHC scores was as follows: IHC 0 (45%), IHC 1 (20%), IHC 2 (13%) and IHC 3 (20%). Two samples (2%) could not be evaluated. PD-L1 positivity defined as ≥ 5% PD-L1 expression on TCs resulted in 23% (22/94) and 34% (33/97) PD-L1Only little tumor infiltrating ICs expressed PD-L1 (SP142 clone) with IHC 0 in 76 cases (77%), IHC 1 in 9 cases (9%), IHC 2 in one case (1%) and IHC 3 in one case (1%). Twelve samples (12%) were not evaluable. Similar to the SP142 clone, low expression was observed on ICs for the 22C3 clone: IHC 0 in 66% of cases, IHC 1 in 16% of cases, IHC 2 in 3% of cases and no IHC 3. Thirteen samples could not be evaluated.P < 0.0001) and 22C3 clone (P = 0.0649). Next, positive SP142 PD-L1 expression was associated with poorly differentiated/basaloid tumors  whereas positive 22C3 PD-L1 expression on TCs was most common in elder patients  and in poorly differentiated/basaloid tumors .A positive link was found between HPV status and PD-L1 positivity on TCs for the SP142 clone , CD3+ T cell count (P = 0.0009 and P = 0.0044), CD8+ T cell count (P < 0.0001 and P = 0.0001) and FoxP3+ T cell count (P = 0.0004 and P = 0.0094) for the SP142 and 22C3 clone, respectively.Notably, PD-L1 positivity on TCs was linked with the immune environment, as this parameter was associated with increased TIL count  of 9.8 years versus 4.1 years for SP142 PD-L1 negativity  versus patients with a low CD8+ T cell count . As CD8+ T cell count proved the strongest marker of the immune cell infiltrate, an analysis was performed combining CD8+ T cell count and PD-L1 expression on TCs. The combined analysis of SP142 PD-L1 expression on TCs and CD8+ T cell count showed that this combination was significantly correlated with OS: median OS of 9.8 years, not reached, 2.4 years and 4.0 years for the PD-L1+/high CD8+, PD-L1−/high CD8+, PD-L1+/low CD8+, and PD-L1−/low CD8+ cohorts, respectively  whereas SP142 PD-L1 positivity and other immune cell counts did not contribute to the model. When assessing the combination of SP142 PD-L1 expression and CD8+ T cell count in the multivariate model, patients with ‘at least one variable positive’ had an OS benefit compared with PD-L1−/low CD8+ patients .In the multivariate model, which adjusted for HPV status, CD8An overview of all multivariate HR is given in Table No relation was demonstrated between PD-L1 expression (SP142 or 22C3 clone) on ICs and clinicopathological variables, including TILs, overall and disease-free survival.+ according to the 22C3 clone (≥ 5 % cut-off). The number of PD-L1+ samples is considerably lower in comparison to other previously reported data on the expression of PD-L1 in OSCC [In our study, positive PD-L1 expression on TCs using the SP142 clone was noticed in 23% of specimens whereas 34% of specimens were PD-L1 in OSCC , 20. The in OSCC . When co−). The expression of PD-L1 on tumor infiltrating ICs is well known but further research is warranted in order to evaluate its contribution to the (anti-) tumor immune response and its link with PD-L1 expression on TCs.The study of PD-L1 expression on ICs did not add useful data to our study, probably because of the very low expression levels  activation of an endogenous inflammatory immune response at the tumor site [Survival analysis revealed that PD-L1 positivity of TCs, using the SP142 clone, was related to better OS but not with DFS in univariate analysis. Also high CD8mor site , 24.To our knowledge, this is the first study to evaluate the expression of PD-L1 in OSCC using two monoclonal Abs (SP142 clone and 22C3 clone) from FDA approved kits that are currently used in various clinical trials. Pharmacodiagnostic testing for anti-PD-(L)1 therapy is nowadays mainly performed with two clones from Agilent-DAKO and two clones from Roche. Recently, it was reported that staining results for the 22C3 clone are in concordance with those obtained from the 28-8 and SP263 clone, but not with those of the SP142 clone , 26 and Also, we deliberately chose to work with formalin-fixed, paraffin-embedded (FFPE) samples from biopsies or resections as this is the material that will be available for prognostic biomarker testing in the future. Although biopsy samples might harbor sampling errors and suffer from spatial heterogeneity in PD-L1 expression, it is important to include these samples in the trials to avoid selection bias. It remains however important to evaluate the concordance of PD-L1 expression between different available clones, between different cut-off values and between samples from different origins (e.g. biopsy versus resection specimens).+ TILs constitute a strong positive prognostic marker in patients diagnosed with OSCC. Furthermore, PD-L1 expression on TCs, assessed by the SP142 clone, also tends to have a positive effect on patient outcome in OSCC. The results of this retrospective study should be confirmed in prospective trials to assess the clinical impact of the biomarkers on routine daily practice. Herein, special attention should be given to methodological issues on PD-L1 testing that were also of concern in this study, such as choice of Ab clone, interpretation criteria and sample type.In conclusion our results demonstrate that CD8n = 27), if not, endoscopic biopsy material was used (n = 72).Ninety-nine patients with histologically proven OSCC (anno 2004-2013) were selected from the archival database of the Department of Pathology, Ghent University Hospital. Patients were excluded from the study in case of recurrent or distant metastatic disease. When available, samples from resection specimens were preferred for further analysis  by either tumor resection with or without lymph node dissection  or lymph node dissection alone (n = 24). Forty-six out of 51 surgically patients received adjuvant treatment, that is, concurrent chemoradiotherapy (n = 30) or radiotherapy alone (n = 16). Clinical data and treatment details are presented in Table Clinical, treatment and follow-up data were retrieved from the electronic patient data file. Tumor sites consisted of tonsil, tongue base, other  or multiple subsites involved. The majority of the patients was treated surgically n = 5 by eithein situ hybridization (ISH) using the Inform HPV III Family B probe  on a BenchMark XT automated stainer  under ISO15189:2012 accreditation. The probe cocktail detects HPV types 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58 and 66. The high-risk HPV ISH test was considered positive if a discrete, blue colored, precipitated reaction product within the TCs was observed.HPV status was determined on FFPE material by high-risk HPV Immunohistochemistry for PD-L1 and TILs subtyping was performed using the BenchMark XT instrument . Staining of CD3, CD8 and FoxP3  was performed on FFPE tissue slides of 2μm as previously described . For staFirst, a hematoxylin-eosin slide was evaluated to confirm the presence of invasive squamous cell carcinoma, to determine the grade of tumor differentiation and/or define basaloid subtype. The infiltration of TILs in tumor stroma was scored semi-quantitatively with a 4-tiered scale (1+ to 4+) as previously described , 30. Rep+, CD8+ and FoxP3+ T cell count. For the subtyping of the immune infiltrate, three representative fields were selected and scored using a similar 4-tiered semi-quantitative method as for TIL scoring (1+ to 4+) [Elaborate evaluation of the immune infiltrate in the stromal compartment , was done semi-quantitatively by scoring for CD3+ to 4+) , 30  to ensure objective scoring as the expression of PD-L1 on ICs was low. PD-L1 staining was quantified in the three same representative fields of intertumoral stroma used for scoring of the T cell infiltrate. Scores were given as percentages reflecting the ratio of PD-L1 staining surface area to the whole surface area of the representative field. The final score was determined as the mean of the separate scores from the chosen fields. Again, cut-off values for PD-L1 positivity on ICs were taken at ≥ 5%.Subsequently, samples were scored for PD-L1 expression with both the SP142 clone and the 22C3 clone by two independent observers. IHC was scored 0 if < 1% of TCs were positive, IHC 1 if ≥ 1% but < 5% of TCs were positive, IHC 2 if ≥ 5% but < 10% of TCs were positive or IHC 3 if ≥ 10% of TCs were positive  has shown that 106 patients are needed to achieve a power of 90% . Associations between categorical variables were determined via Chi-square test. In case of 2 × 2 contingency tables with 1 or more observed values lower than 10, a Fisher's Exact test was applied. Secondly, the HR of immunohistochemical parameters on OS and DFS was determined by a log-rank (Mantel–Cox) test. OS was calculated from time from diagnosis on biopsy until day of death or final follow-up whereas DFS was calculated from time of therapy until time of recurrence. For every patient who was not seen in clinic for the preceding 12 months at the point of the survival analysis, the general practitioner was contacted to confirm whether the patient was still alive. Patients that were lost to follow up at either the Ghent University Hospital or the general practitioner were censored in the survival analysis (n = 8). Finally, the covariate effect of the risk factors on survival analysis, that reached a P value lower than 0.1 on univariate log-rank test, was determined by means of the Cox proportional hazard model (backward method). Samples that were classified as ‘ITE’ were not included in the analysis.Sample size calculation for a proportional


Immune checkpoint inhibitors targeting the PD-1 pathway have greatly changed clinical management of metastatic urothelial carcinoma and metastatic renal cell carcinoma. However, response rates are low, and biomarkers are needed to predict for treatment response. Immunohistochemical quantification of PD-L1 was developed as a promising biomarker in early clinical trials, but many shortcomings of the four different assays  have limited its clinical utility. Further limitations include the use of archival specimens to measure this dynamic biomarker. Indeed, until PD-L1 testing is standardized and can consistently predict treatment outcome, the currently available PD-L1 assays are not clinically useful in urothelial and renal cell carcinoma. Other more promising biomarkers include tumor mutational burden, profiles of tumor infiltrating lymphocytes, molecular subtypes, and PD-L2. Potentially, a composite biomarker may be best but will need prospective testing to validate such a biomarker. Over the past few years, immune checkpoint inhibitors (CPIs) blocking the programmed cell death receptor 1 (PD-1) pathway have emerged as treatments capable of improving the overall survival of patients with metastatic renal cell carcinoma (mRCC) and metastatic urothelial carcinoma (mUC) . Based oPD-L1 is one of several ligands for the PD-1 receptor, and early trials in advanced mUC suggested that immunohistochemistry (IHC) staining for PD-L1 on paraffin-embedded tumor specimens would serve as a predictive biomarker , 6. BaseThe phase I study of atezolizumab in mUC was initially designed to include PD-L1-positive enriched cohorts, with a dose expansion cohort for all mUC patients regardless of PD-L1 status . The Venp = 0.41) [p = 0.038) [IMvigor 210 (Cohort 2) and KEYNOTE-045 explored the use of atezolizumab and pembrolizumab, respectively, in the post-platinum mUC population. IMvigor 210 enrolled patients with locally advanced or mUC refractory to cisplatin-based chemotherapy. The SP142 rabbit antibody IHC assay was used to assess PD-L1 status in archival specimens as discussed above; PD-L1 positivity was defined as ≥10% PD-L1 positive immune cells in the tumor microenvironment (defined as 3+ in the phase I study of atezolizumab). While the objective response rate (ORR) of the entire cohort was 15%, the ORR was 26% (26/100) in PD-L1 positive patients, compared with only 9% (19/210) in PD-L1 negative patients (Table  = 0.41) . Interes= 0.038) . It is sPD-L1 status did not predict for response in KEYNOTE-045 . More imWhile patients with a positive Ventana SP142 PD-L1 test were almost three times more likely to respond to atezolizumab than those who were PD-L1 negative in IMvigor 210, the absolute difference in ORR between PD-L1 positive versus PD-L1 negative patients was not as large as seen in the phase 1 atezolizumab study. Furthermore, Dako 22C3 PD-L1 expression did not appear to be associated with any treatment response for pembrolizumab in KEYNOTE-045. Assuming that atezolizumab and pembrolizumab have similar clinical activity, it is worth exploring why, PD-L1 “positive” patients are not the same, and why these biomarkers may not have sufficient discriminatory power to predict response to a CPI. As discussed above, these IHC assays used completely different antibodies, and moreover, the assay characteristics define PD-L1 positivity based on staining different cellular populations (tumor cells versus immune cells versus tumor and immune cells). Yet another discrepancy includes the percentage of positive cells for the threshold of positivity within the assay.Three additional CPIs have now been studied in and received accelerated FDA approval for the platinum-refractory population of mUC – nivolumab, durvalumab, and avelumab , 9, 10. For example, in the durvalumab trial, 51% of patients were defined as PD-L1 positive, compared with only 16% of patients in the avelumab trial , 10. ThiDue to a variety of factors, including renal or hearing impairment, poor performance status, and neuropathies, 30% to 50% of patients with chemotherapy-naïve advanced UC are not candidates for platinum-based chemotherapy . Cohort Based on the data presented above, in which patients with mUC may respond to PD-1/PD-L1 blockade even if their archival tumor lacks PD-L1 expression, we do not recommend PD-L1 clinical testing in UC patients. While in some studies PD-L1 positivity may identify patients more likely to have an objective response, and combined tumor/microenvironment testing may further enrich for responders, in others studies this biomarker has no discriminatory power, and given the conflicting results treatment with a CPI should not be withheld based on PD-L1 status in mUC. Prospective studies of PD-L1 as a predictive biomarker are needed, with consideration for contemporary/recent biopsies, tumor heterogeneity assessments, and expression in tumor vs normal immune cells.p = 0.002) [Nivolumab received FDA approval for the treatment of patients with advanced RCC refractory to first-line vascular endothelial growth factor (VEGF) inhibitors, demonstrating improved OS compared to everolimus  . Tumor P= 0.002) . Nivolum= 0.002) . Therefo= 0.002) , 17. TheAtezolizumab has also been investigated in mRCC. The expansion cohort of a phase Ia trial enrolled 70 patients with treatment refractory mRCC; all patients were treated with atezolizumab . Enrollmp = 0.095) and 1.03  for the atezolizumab alone vs sunitinib arm. Early phase trials combining immune checkpoint inhibitors with small molecule VEGF receptor inhibitors inpatients enrolled regardless of PD-L1 status have shown promising results with ORRs ranging from 67% to 100%. These studies demonstrate a signal for potentially improved overall response rates for patients treated with combination therapy. Several phase III studies are currently underway investigating checkpoint inhibitors in combination with VEGF-targeted therapy for patients with mRCC [Atezolizumab has also been investigated in the frontline setting in combination with bevacizumab, a VEGF inhibitor . Bevacizith mRCC –24.p < 0.0001) and ORR  in patients with intermediate-high risk disease [p = 0.98), whereas in PD-L1 positive population, there was a large difference in PFS between these two groups  [p = 0.0002) [Immunotherapy CPI combinations have proven effective in melanoma, and CheckMate-214 was the first in mRCC to use combination of CTLA-4 and PD-1 inhibitors. CHECKMATE 214 was a phase III trial which randomized 1040 patients with metastatic clear cell RCC to treatment with either combination nivolumab-ipilimumab or sunitinib. Co-primary endpoints included ORR, progression free survival (PFS), and OS, specifically in the IMDC intermediate or high risk population. Secondary endpoints included PFS and OS for the intention to treat population (including favorable risk). Nivolumab-ipilimumab improved both median OS (not reached (NR) vs 26.0 months, HR 0.63,  disease . PD-L1 p disease . In the  0.0003) . However 0.0002) . FurtherFurther correlative work has also emphasized the ineffectiveness of PD-L1 as a predictive biomarker in mRCC . PrimaryThe clinical trials for mUC and mRCC mentioned above highlight the numerous challenges facing PD-L1 as a biomarker. First, there is no standardized format to assess PD-L1 immunohistochemically. Several pharmaceutical companies have established partnerships with diagnostic companies to design companion PD-L1 assays. These assays are in a sense more different than they are similar. The only similarity between the assays is that they are all IHC tissue-based assays to detect membrane expression of PD-L1. Critical differences include which components are included for the purposes of scoring (tumor cells vs. immune cells), as well as staining thresholds to define PD-L1 positivity . Thus, iSecond, because these clinical trials have used different companion diagnostic assays, we are unable to indirectly compare the effectiveness of one drug in a “PD-L1 positive” population compared to another drug in a “PD-L1 positive” population. The first two concerns have been ameliorated in the diagnostic testing for non-small cell lung cancer (NSCLC). Ratcliffe et al. studied the concordance between three commercially available PD-L1 IHC assays for NSCLC patients including Ventana SP263 , Dako 22C3 (pembrolizumab), and Dako 28–8 (nivolumab) and found >90% concordance at several levels  of PD-L1 expression . The sepLastly, there are both biological and technical challenges which further complicate PD-L1 testing. This has been well reviewed in the literature , 33. BioThere are several other predictive biomarkers under examination for mUC and mRCC, including tumor mutational burden (TMB), mismatch repair status, gene expression profiles (GEP), TCGA (The Cancer Genome Atlas) profiling, tumor infiltration lymphocytes (TILs), and PD-L2.It has been observed that tumors with a higher mutational burden may have a better response to immunotherapy . Higher p = 0.0041) [p = 0.002) and median PFS (3.02 months vs. 1.87 months) [ERBB2 (HER2) and ERBB3 (HER3) mutations [In mUC, data from IMvigor 210 further suggest that high TMB may not be only prognostic of survival, but potentially predictive of response , 13. Pat months) . Recentlutations . These dNot surprisingly, patients with the highest mutational burden often harbor specific DNA damage response defects, such as microsatellite instability (MSI-H) or are mismatch repair deficient (dMMR) . Thus, cGene expression profiling has demonstrated utility as a predictive biomarker to chemotherapy and immunotherapy –47. The p < 0.0001). GEP score could correctly predict 70 of the 81 responders, compared to the PD-L1 CPS cutoff, which predicted only 41 of the 81 responders. While this is hypothesis generating, the utility of GEP still needs to be validated in large prospective clinical trials.Ayers et al. evaluated 680 tumor and immune related genes using a NanoString nCounter platform and found that the genes most able to separate responders from non-responders of PD-1 therapy were genes linked to interferon gamma signaling . The inip < 0.0001) and increased PD-L1 immune cell prevalence . However, the increase of PD-L1 expression in the basal subtype did not correlate with an objective response. Response to atezolizumab in this post-platinum population was much higher in the luminal cluster II subtype compared with all other subtypes. This relationship was again observed in the front line platinum-ineligible population with the luminal II samples having a higher response rate compared to the other molecular subtypes – however, the sample size was not sufficient to reach statistical significance [Consensus clustering based on gene expression data from TCGA has distinguished two major intrinsic subtypes of high grade bladder cancer – luminal and basal . Rosenbeificance . FurtherPD-L2 is another ligand capable of binding to PD-1. While its structure is quite similar to PD-L1, the exact function of PD-L2 is controversial . PD-L2 pIn RCC, while PD-L1 expression was observed in only 9% of cases in a series of 425 patients, PD-L2 expression was observed in 50% of cases . PD-L1 aCPIs are known to enhance the activity of the adaptive immune system by recruiting CD8 positive cytolytic T cells into the tumor microenvironment . CD8 posp = 0.0265) [High T cell infiltration and clonality in advanced UC has also correlated with treatment response . Increas 0.0265) . These tUltimately, a single biomarker may not adequately predict response to PD-1 immune checkpoint therapies, and a combination of factors may need to be taken into account to predict responses. Based on the level of CD8A and PD-L1 expression, Ock et al. described four distinct tumor microenvironment immune types (TMIT) . Tumors B. fragilis, the mice were able to recover an anticancer response to CTLA-4 blockade. This finding is similar to that reported by Sivan et al., which found that the best responses to PD-L1 blockade required specific Bacteroides - mice which were given oral Bifidobacterium improved tumor control to the same degree as those given an anti-PD-L1 agent [It is well established that the gut microbiota plays a critical role in both innate and adaptive immune homeostasis . Thus, iL1 agent . With reL1 agent . These sThe FDA approval of several checkpoint inhibitors for the treatment of mUC and mRCC represents a major landmark for patients and oncologists. However, it is critical that we choose the right patients, given that these drugs have the potential for not only physical toxicity but also financial toxicity, with costs of up to $20,000 per month . Selecti


Graphene supports long spin lifetimes and long diffusion lengths at room temperature, making it highly promising for spintronics. However, making graphene magnetic remains a principal challenge despite the many proposed solutions. Among these, graphene with zig-zag edges and ripples are the most promising candidates, as zig-zag edges are predicted to host spin-polarized electronic states, and spin–orbit coupling can be induced by ripples. Here we investigate the magnetoresistance of graphene grown on technologically relevant SiC/Si(001) wafers, where inherent nanodomain boundaries sandwich zig-zag structures between adjacent ripples of large curvature. Localized states at the nanodomain boundaries result in an unprecedented positive in-plane magnetoresistance with a strong temperature dependence. Our work may offer a tantalizing way to add the spin degree of freedom to graphene. Owing to long spin lifetime and diffusion length, graphene holds promise for spintronics, yet it does not possess intrinsic magnetic properties. Here, the authors observe experimentally a large, positive magnetoresistance at graphene nanodomain boundaries. This means that inducing magnetic moments in graphene is non-trivial, and there have been many studies suggesting how this could be achieved, such as the introduction of vacancy defects131415171819212324252627pz orbitals with px and py orbitals from the σ-band30323132343637232425262728Spin-transport electronics, or spintronics, is a subject of active international interest and investigation. Utilizing electrons' spin degree of freedom would give rise to new and more efficient forms of logic and storage devices245678911In this paper, we investigate the magnetoresistance (MR) of graphene synthesized on cubic-SiC(001). STM characterization indicates that a zig-zag structure forms on one side of our NBs and, moreover, ripples with large curvature are formed on both sides adjacent to the NBs. An unprecedented positive in-plane MR with a strong temperature dependence was observed. This positive MR decreases as the temperature is reduced from 300 to 100 K, reaching zero at 100 K, and increases again in the temperature range from 100 to 10 K. From non-equilibrium Green's function calculations, we show that the MR arises from the contributions of the NBs, which can explain the one-dimensional (1D) transport behaviour we observe at low temperatures. Above 100 K, the Zeeman effect leads to the inequivalent confinement of electrons with different spins from the two-dimensional (2D) sheet to the 1D NBs, which produces the positive MR observed at higher temperatures. This behaviour shows that it is possible to engineer new tunable electronic and magnetic nanostructures purely from graphene.2 V−1 s−1 at 10 K and 60 cm2 V−1 s−1 at 300 K, and the corresponding current densities are 2 × 1012 cm−2 at 10 K and 2 × 1013 cm−2 at room temperature. This value for electron mobility is notably less than the ∼1,000 cm2 V−1 s−1 observed for trilayer graphene prepared by mechanical exfoliationB2) in B dependence at low field and a quadratic B2 dependence at high field. To rule out the possibility that this effect might be due to misalignment between the magnetic field and graphene plane, θ=0°) to an in-plane  configuration, where θ is the angle between the direction of the magnetic field and the direction normal to the graphene plane. The MR exhibits a strong dependence on θ and the MR ratio increases with decreasing θ. For θ<60°, the MR ratio is always negative because of a reduction in scattering at the NBs. Therefore, misalignment between magnetic field and graphene plane can be ruled out as the source of the effect. A similar phenomenon was observed at other temperatures  modelR0 and C are the fitting constants, indicating that the transport below 150 K is restricted to the NBs. In contrast to the low-temperature data, the R–T curve above 150 K has a different slope and is fitted better by the cube root expression z direction at high enough temperatures. To verify the nature of the transport mechanisms below and above 150 K, I–V curves and the corresponding dV/dI measured at different temperatures. Above 150 K, the current increases linearly with applied voltage, clearly highlighting the 2D transport mechanism in this temperature range. The linear I–V curves also indicate good Ohmic contact between the electrodes and graphene. Interestingly, below 100 K, nonlinear I–V curves were observed and the dV/dI shows a maximum at I=0 mA, indicating the presence of a charge transport gap below 100 K. At low temperatures, the NBs have 1D localized edge statesTo investigate the effect of the NBs on the magneto-transport properties, σ under an in-plane field for massless Dirac fermions in pristine graphene should give a positive Δσ(B) and thus a negative MRσ(B), which requires a sublinear behaviour σ(μ)∝μα with 0<α<1, where μ is the chemical potential. Details of the calculation can be found in the B at low fields and a quadratic B dependence at high field. Moreover, a rather strong temperature dependence is also observed. The most reasonable explanation for the positive MR comes from the intrinsic properties of the NBs themselves and that the carriers have different transport mechanisms below and above 150 K due to the presence of the NBs. Here we explain how the NBs can modify the Δσ(μ) and the MR of graphene by calculating the transport properties of graphene containing a single NB based on a NEGF calculation and the Landauer–Keldysh formalismB dependence at low field and B2 dependence at high field is predicted by the calculation in agreement with what was experimentally observed. We simulated different lengths of the pristine graphene while maintaining the dimensions of the NB and found that the MR dependence remains exactly the same (σ(μ) as a function of μ for B=4 T, confirming that for graphene with NBs, σ(μ) has a sublinear behaviour σ(μ)∝μα with α≈0.839. Thus, we can conclude that the positive MR under an in-plane magnetic field at low temperatures arises predominately from the NB itself. 58In general, according to the Kubo–Greenwood formulathe same . Figure I–V characterizationE0) and the first excited state (E1) for each quantum well will thus be on the order of 0.1 eV as estimated from the finite-depth quantum well confinement. Once energized by an applied bias or thermal agitation, electrons can jump to the first excited state, and the wave functions of the localized edge states associated with the NBs begin to smear out more readily . Under an in-plane field, we observed a positive MR with a strong temperature dependence. Using NEGF calculations combined with experimental data, we have shown that the NBs with ripples have the potential to work as a spin filter and can result in a positive MR at low temperature. Moreover, our work suggests that graphene with NBs has localized states and large spin–orbit interaction at the ripples. The confinement of electrons of a particular spin direction from 2D to 1D NBs by the Zeeman effect is responsible for the positive MR observed at high temperatures.−11 Torr. The pressure during the graphene synthesis at sample temperatures of 1,300–1,350 °C did not exceed 4 × 10−10 Torr. STM characterization of the graphene trilayer synthesized on SiC(001) was carried out in situ at room temperature using single crystalline W[111] and W[001] tips prepared using chemical etching and UHV sharpening. Low energy electron microscopy (LEEM) measurements were done using a SPELEEM microscope (Elmitec GmbH) installed on I311 at the MAX laboratory in Sweden. Before the LEEM characterization, the trilayer graphene/SiC(001) samples were annealed in the microscope's UHV chamber at temperatures ranging from 600 to 1,000 °C to remove possible contamination.The uniform trilayer graphene was fabricated on a cubic-SiC thin film with a thickness of ∼1 μm grown on on-plane Si(001) wafers using Si-atom sublimation followed by high-temperature surface graphitization in the ultra-high vacuum (UHV) chamber of a room-temperature GPI-300 scanning tunnelling microscope352 plasma etching for 60 s (8 W at P=20 mTorr). The nanocontact fabrication was carried out by electron beam lithography using single-layer-positive tone resist poly(methyl methacrylate) (PMMA) supplied by MicroChem Corp. After development, thick metal contacts consisting of Ti (5 nm)/Au (45 nm) were deposited through e-beam evaporation.The field effect transistor Hall bar structure was fabricated by electron beam lithography using a single-layer negative tone resist ma-N 2403 supplied by MicroChem Corp, followed by Ot equal to 2.7 eV, and the system Hamiltonian isA tight-binding model was used to describe the system with a nearest neighbour hopping energy Ci†(Ci) creates (annihilates) an electron at site i and Ui is the onsite energy at site i. The onsite energy in the graphene system is the energy of the Pz orbital due to position variance6263t and Ui values can be calculated by taking into account the position variance caused by NBs or their substrate and so ong is the Landé g-factor, B is the external magnetic field and σ is the Pauli matrix. The last term in the Hamiltonian is Rashba spin–orbital coupling, induced by the ripple near the NB, αR is the SOC strength. In the simulation we consider that the NB and its nearby graphene two-layer neighbours both have Rashba SOC. Note that no phonons were involved in our calculations. We use the NEGF formalism to calculate magnetoresistence53where GR (GA) is the retarded (advanced) Green's function of the systemΓp(q) is defined as Σp(q) is the self-energy of the lead. From the transmision T(E), we can get the conductance G(E)=G0T(E), where fp(q) is Fermi distrbution of each lead. In our MR calculations, the bias V is fixed at 0.4 V and the resistance is obtained by R(B)=V/I(B). The conductivity σ(μ) is proportional to the transmission and can be calculated from σ(μ)∼T(E=μ). The charge density is calculated by the local occupation number at each site. The occupation number operator at site i is G< is the lesser Green's function whose formula is G<(E)=GR(E)Σ<(E)GA(E), where i is σ= are the Pauli matrices, and Trs[…] denotes the trace in the 2 × 2 spin −½ space.where The data that support the findings of this study are available from the corresponding author upon request.How to cite this article: Wu, H.-C. et al. Large positive in-plane magnetoresistance induced by localized states at nanodomain boundaries in graphene. Nat. Commun.8, 14453 doi: 10.1038/ncomms14453 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary Figures, Supplementary Notes and Supplementary References


Climate change is expected to influence spatial and temporal distributions of fish stocks. The aim of this paper is to compare climate change impact on a fishery with other factors impacting the performance of fishing fleets. The fishery in question is the Northeast Arctic cod fishery, a well-documented fishery where data on spatial and temporal distributions are available. A cellular automata model is developed for the purpose of mimicking possible distributional patterns and different management alternatives are studied under varying assumptions on the fleets’ fishing aptitude. Fisheries management and fishing aptitude, also including technological development and local knowledge, turn out to have the greatest impact on the spatial distribution of the fishing effort, when comparing the IPCC’s SRES A1B scenario with repeated sequences of the current environmental situation over a period of 45 years. In both cases, the highest profits in the simulation period of 45 years are obtained at low exploitation levels and moderate fishing aptitude.The online version of this article (doi:10.1007/s13280-017-0955-1) contains supplementary material, which is available to authorised users. It is difficult to predict future development of Arctic marine ecosystems and, even more so, how these are affected by human interactions. Immediate effects of such interactions are not only functions of the level and profile of the human activity but also of current state and dynamics of the natural system. Spatial and temporal distributions of prey and predator species vary, depending both on external drivers . Climate change could cause system perturbations, redistributing some, or large, parts of the systems sample space. A dramatic change in the sample space of the system may be referred to as an ecosystem shift  stock in the Barents Sea, the resilience of the Northern cod fishery is confirmed by archaeological fishbone analyses  cod fishery, the most important fishery in the Barents Sea. The NEA cod stock employs a variety of different coping strategies to adapt to a fluctuating physical and biological environment such as spawning and feeding migratory patterns, cannibalism, maturation dynamics and opportunistic feeding strategies . The climate scenario is based on the IPCC AR4 SRES A1B scenario Anon.  which atThe focus on spatial distributions and fleet diversity is motivated from the widespread expectation that northern fish species will shift to a more northern distribution caused by increased water temperatures  covering biological growth and spatial and temporal distribution of the cod stock. This model is run together with an agent-based model (ABe: Agent-Based economic model) defined within the same lattice, covering the economic exploitation of the stock. The flow chart of the combined CAb-ABe-model and the connected SinMod model is shown in Fig. SinMod model  with rules based on a Moore neighbourhood of range two. Each cell is defined in terms of geographical coordinates and the state variable of the cell is the cod biomass in the water column at the geographical position of the cell. Hence, the spatial distribution of the cod biomass at one point in time is given by the matrix of state variables in the lattice. According to the definition of Moore neighbourhoods  and ecosystem survey (during August/September). Age-structured data from these data sources have been aggregated for the purpose of parameterising the CAb-ABe model. Registered catches and survey data have been spatially interpolated by Radial Basis Function interpolation .Information on spatial distribution of NEA cod for the period 2004–2010 has been provided through the on Myers  followedThe data sample from the period 2004 to 2010 was considered to represent the current environmental situation, rather than reflecting ongoing changes in climate. There are several reasons for this. The period is rather short and the datasets, although displaying significant variations in the distributional patterns, do not show any significant trends or systematic changes. The seasonal variations are extreme but the seasonal biomass centres of gravity are almost identical each year during the period. In terms of weighted biomass distances for each quarter during the time period from a given geographical point , cluster analyses did not reveal any systematic changes and different years constituted the main cluster for each of the four quarters.SinMod and FishExchanges are made publicly available through UiT Open Research Data.Based on this, the data sample was considered as a representative distribution related to the current climatic conditions. The average monthly spatial distributions of NEA cod stock biomasses during the period 2004–2010 were found by merging the different sources of information relevant for each month as explained in Eide . ResultiSpatial and temporal distributions of the NEA cod environmental carrying capacity levels for each scenario have been estimated on the basis of constraining physical and biological factors in addition to the observed distributional patterns Fig. . The NEASinMod datasets of bathymetry, temperatures and zooplankton biomasses over the simulation period, representing the changes in environmental carrying capacities corresponding to the A1B scenario. As stochastic element is added to estimated environmental carrying capacities. As the mean deviation of the carrying capacity of each cell varies between 20 and 30 %  during the period of observations (2004–2010), a normally distributed stochastic element with a mean value of one and a standard deviation of 10 % is assumed. The stochastic element also serves to establish the zero scenario monthly carrying capacities, repeating the current climate with the minor perturbations caused by the stochastic process.Monthly estimated current carrying capacities are then modified according to Figure When having established the cellular automata lattice with cell-specific carrying capacities, which develop according to environmental variables and observed distributional pattern in the cod population, the next step is to establish cellular automata distributional rules. Essentially, the rules describe how individual cod moves in terms of directions and distances within the time frame of 1 month. According to Rose et al. , NEA codThe rules should in principle be able to move the cod biomasses over time according to previous observations. This boils down to a straight forward statistical problem minimising the sum of squared distances between the observed centres of gravity in the observed cod biomass and the centres of gravity in the by rule distributed cod biomass  and two fleet types  placed in each of these ports  per unit of harvest. The fleet revenue (re) obtained in cell i isvc) of the fishing operation isi.Similarly to Heen and Flaaten , Hannesscm) of all cells isThe fleet contribution margin of all cells are found by Eqs.  and 3)  3) when e and x give the fishing effort of the fleet and stock biomasses distributed on cells and months. Index m indicates month number and n is the total number of cells available for the given fleet. Number of available cells depends both on the physical range of the vessel  from the contribution margin described in Eq. :5\documet (a given month in a given year) is the sum of the fishing effort distributed on all available cells:Total fleet fishing effort at time V. The relation between absolute fleet size, V, and utilised fishing effort, E, isThe fleet capacity in terms of maximum fishing effort which may be produced during a single month is E/V) may, however, bring the resulting dynamics closer to the dynamics assumed by Smith, since also fleet utilisation varies in space and time (e.g. negative contribution margins keep vessels in harbour). After introducing the entry (fg) and exit (fd) rates, the fleet dynamics are given byThis study assumes a pure or quota-regulated, open access fishery. Entry to and exit from the fishery are driven by profits beyond the normal level or negative profits, respectively. While Vernon Smith in his seminal paper Smith  assumed Entry rates are often expected to be higher than exit rates as in Eide .s. The core expression for each vessel group in the model is given bys, reflecting the fleets aptitude of identifying the most profitable (in terms of the revenue/cost ratio) fishing grounds. The smartness parameter (s) is a lump-based parameter where a number of features are reduced down to the value of this single parameter. The two extremes (s = 0 and s = ∞) go from a uniform distribution of fishing activities in the area available for the fleet  to placing all fishing activities into one single cell . For the special situation s = 1, the distribution of fishing activities exactly follows the distribution of profit opportunities (expressed by revenue/cost ratios).A reasonable assumption is that the fishers attempt to maximise their economic performance by fishing at the most profitable areas (e.g. within the most profitable cells). The problem is however to identify where the most profitable cells are positioned. The fishers search to solve this problem through the use of their best knowledge, experience and skills, including the use of fish finding technology, the information that may be obtained within the fishing community and from other sources, attitude towards risk and the economic factors constraining their activity. How successful the fishers are in identifying the most profitable areas depends in this model on the value of a single parameter, the smartness parameter entclass1pt{minima of Eqs.  and the s = 1 is regarded to be the lowest smartness level of interest, while a possibly unrealistically high level of s = 10 is the highest smartness level included in the study. The range In the following, F), assuming perfect stock information.The study includes different governmental constraints represented by four different management regimes of which one is no management (open access). The other three management regimes are all in principle structured similarly to the current management system, assuming different exploitation rates and perfect management control. A total allowable catch (TAC) is set according to a given target levels of the fishing mortality rate  vessels and high sea (large) vessel in a fixed ratio (60/40), which is a slight simplification of the current quota allocation system. The high sea vessels are not allowed to fish inside four nautical miles from the baseline, which is implemented by limited access  to cells along the coast.This study investigates and compares distribution and variability in the two scenarios, in particular emphasising fleet diversity and spatial distribution of the fishing activity. While previous studies Eide , 2008 sus-values 1, 1.5, 2, 3, 5 and 10) and four management regimes . The fleet dynamics is in all simulations controlled as described above, inducing a total fleet capacity  which may be larger or equal to the active fleet E: V) and the fishing effort (E) follow different paths in different fishing ports and for the two types of fleets. The Shannon function H is used as a fleet diversity index  equal 0.2 and 0.4. This effect is however not apparent in the case of F = 0.1 in which available stock biomass already is stabilised on a quite high level . When comparing the profit surfaces of the two scenarios for all the years  with the last 25 years of the simulation , it becomes visible how the environmental effect contributes in lifting the whole profit surface of the A1B scenario.The years after the environmental shift in the mid-thirties provides however the fleets with considerably higher profits in the A1B scenario than what is obtained in the zero scenario . In general, the A1B scenario seems to give relatively larger benefits to higher smartness levels than the zero scenario does. In both cases, highest profits are found at the fishing mortality rate (F) 0.2 but while the smartness level in A1B scenario maximum is 5 it is 1.5 in the zero scenario.Open access fishery combined with high smartness levels results in higher profits in the zero scenario than in the A1B scenario throughout the simulation period. At lower smartness levels, however, the profit surface area within the open access area reaches surprisingly high levels as seen in the lower-right table in Fig. S1 also indicates, for both scenarios, that the largest profits are obtained at moderate levels of the smartness parameter s, in the range 1–3 in the zero scenario and 1–5 in the A1B scenario. The exception is for the last 25 years of the simulation period, when also higher exploitation levels contribute in large profits in the A1B scenario.Figure S2 of the Electronic Supplementary Material, differing between vessels belonging to the coastal and high sea fishing fleets. As higher values of the diversity index indicate higher diversity, clearly the coastal fleet exhibits the highest diversity at low exploitation levels and for low smartness values at all levels of exploitation. The two scenarios follow the same pattern in this respect and also regarding the trends while increasing smartness levels. As higher smartness levels seem to contribute in increased fleet diversity for the high sea vessels (for exploitation rates at F = 0.2 and above), the opposite is the case for the coastal fleet, though not so pronounced at F = 0.2 as for the highest exploitation levels. Although the fleet diversity for high smartness levels and open access seems to drop below the corresponding levels of the zero scenario and the opposite for F = 0.4, the general impression is that the fleet diversity in the A1B scenario corresponds very closely to the fleet diversities found in the zero scenario simulations.Eide  introducS2. Each graphical plot in Fig. S2 is divided by the diagonal into two sector where the upper sector is the area where the high sea fleet exhibits a higher diversity than the coastal fleet, while it is opposite in the sector below. In Fig. S2, for the zero scenario as well as for the A1B scenario, at high exploitation rates (open access and when F = 04) and for s-values higher than 1.5, all points lay above the diagonal and hence indicate that the high sea fleet is more diverse than the coastal fleet.The declining diversity for small-scale vessels at higher smartness values and exploitation rates is also clearly visible in Fig. The fact that the high sea fleet has a wider range than the coastal fleet may be the simple explanation of the higher diversity at high levels of exploitation. The advantage of a higher mobility combined with a minimum level of fishing aptitude becomes relatively a more important advantage as the exploitation level increases. As seen in Fig. At low s-values in open access, the fleet diversities of the two scenarios are virtually identical. Overall, the highest fleet diversities are found at low exploitation levels and high smartness levels. The fleet diversities of the two scenarios follow each other closely but from the zero to the A1B scenario, the tendency is increasing diversity in the coastal fleet at low smartness while it is the high sea vessel diversity which increases at high smartness levels.S3 of the Electronic Supplementary Material shows the vertical and horizontal distributional ranges of the gravity centres of stock biomass and fishing effort distributions over the 45 simulated years. The stock distribution in terms of centres of gravity turns out to be very stable, almost not affected of fishing intensity and levels of smartness. A slight North-eastern movement is indicated for the A1B scenario compared with the zero scenario but the main impression is that the stock biomass distribution does not change. In the case of open access, the two scenarios are practically equal in terms of stock biomass distribution.Figure Significantly larger changes are seen in the distribution of fishing effort, also reflecting the changing fleet compositions due to stock properties, exploitation levels and smartness. At increasing levels of smartness, there is a light tendency towards a more South-western distribution of fishing effort in both scenarios, even for the A1B scenario where the stock distribution slightly moves in the opposite direction. This indicates that the effect of reducing cost related to distance from port may be a more important factor than the possibly more North-eastern stock distribution.S3 come out in Fig. S4 of the Electronic Supplementary Material, showing how the stock biomass distribution clusters for the two scenarios and their different combinations of fishing intensity and smartness levels. The two scenarios come out as independent clusters for all smartness levels at the lowest exploitation rate (F = 0.1), while a more mixed picture is seen at higher exploitation levels. At the higher exploitation levels, the differences between the different scenarios and combinations are smaller but still a distinct clustering between scenarios are visible.Some details of the information embedded in Fig. S5 and S6 of the Electronic Supplementary Material), which for natural reasons are closely related. In these cases, the lowest exploitation level and low smartness levels cluster independent of climate scenario. It seems also to be a combined clustering tendency for both scenarios at higher exploitation levels and higher smartness levels, suggesting that the distribution of effort and hence catches is more depending on smartness levels and fishing intensities than marginal changes in the distribution of stock biomasses.This is however not the case for the distribution of catch and effort Below is the link to the electronic supplementary material.