As the technology revolution and industrialization have flourished in the last few decades, the development of humidity nanosensors has become more important for the detection and control of humidity in the industry production line, food preservation, chemistry, agriculture and environmental monitoring. The new nanostructured materials and fabrication in nanosensors are linked to better sensor performance, especially for superior humidity sensing, following the intensive research into the design and synthesis of nanomaterials in the last few years. Various nanomaterials, such as ceramics, polymers, semiconductor and sulfide, carbon-based, triboelectrical nanogenerator (TENG), and MXene, have been studied for their potential ability to sense humidity with structures of nanowires, nanotubes, nanopores, and monolayers. These nanosensors have been synthesized via a wide range of processes, including solution synthesis, anodization, physical vapor deposition (PVD), or chemical vapor deposition (CVD). The sensing mechanism, process improvement and nanostructure modulation of different types of materials are mostly inexhaustible, but they are all inseparable from the goals of the effective response, high sensitivity and low response–recovery time of humidity sensors. In this review, we focus on the sensing mechanism of direct and indirect sensing, various fabrication methods, nanomaterial geometry and recent advances in humidity nanosensors. Various types of capacitive, resistive and optical humidity nanosensors are introduced, alongside illustration of the properties and nanostructures of various materials. The similarities and differences of the humidity-sensitive mechanisms of different types of materials are summarized. Applications such as IoT, and the environmental and human-body monitoring of nanosensors are the development trends for futures advancements. Relative humidity (RH) is expressed as a percentage, which indicates a present state of absolute humidity relative to the saturation level at a given temperature. As the technology revolution and industrialization have flourished, the measurement of RH has become an important issue in industry, food preservation, chemistry, agriculture, environmental monitoring and our daily life ,2,3,4,5.Some materials and measurement types of humidity nanosensor technologies have been researched in the literature; materials including ceramic ,7,8,9,10The humidity nanosensor system  includesThe solution method ,52,53,543), were prepared. The nanoporous structures were prepared from a commercially available AAO template with the spin-coating process to from the AlPcCl dielectric thin film, the schematic cross-sectional diagram is shown in Another example was proposed by Ahmad et al.: to develop the organic nanostructure sensing layer on an AAO template for humidity sensing by Aluminum 1,8,15,22-tetrakis 29H, 31H phthalocyanine chloride  . Two dif2O3  PbIrication . The sen5 KΩ to 90 KΩ at RH% from 11% to 95%. If the Formula (13) is applied to this case, the response can be higher to 200,000%; however, only 100% was calculated using Formula (11). Therefore, some groups define a response-estimation formula in their papers to present the sensor characteristics. For example, Lu et al. [3/TiO2 composite achieved 15723% using Formula (17).The response value can reach to ~1,000,000% in the RH range from 10% to 100%, which is much larger than other works we mentioned except the SnS nanoflake sensor with current-type measurement from 3% to 99% proposed by Tang et al. . The curu et al.  defined Recently, two-dimensional (2D) nanomaterials have played an important role in humidity nanosensors due to the larger specific surface area and high carrier mobility. The nanostructures of carbon-based materials, such as grapheme oxide (GO) , carbon In terms of the optical sensors made of carbon-based materials, these are mainly proposed with wavelength and power measurement using microfiber knot resonators (MKRs) ,119, whiTo solve the energy crisis and environmental-protection problems of using fossil fuels, energy policies in the new era focus on improving green-energy technology ,125,126.The new triboelectric nanogenerator (TENG) technology is successfully used for harvesting wasted energies from motion, sliding, vibration, hydraulic, or air power and has received much attention for effective harvesting energy in numerous practical applications, including consumer electronics, biosensors, pressure sensors, and portable electronic devices. It is an important and a significant issue for TENG devices to develop a cost-effective and rapid processing, to be environmentally friendly, and to develop high-performance technology. The TENG performance-measurement method is shown in In the TENG humidity-sensing field, the mechanism of electrical-signal formation is totally different from the above. The TENG output voltage is mainly affected by the triboelectricity electron transfer from different materials. For the semiconductor- or polymer-based materials we mentioned above, the water-molecule adsorption reduces the resistance on the surface of the sensing material, resulting in an increase in conductance. However, in terms of TENG, the water molecules will restrict the electron transfer and increase the contact resistance from the tribolayers. Therefore, the TENG output voltage will reduce at higher humidities ,42,44.0 stand for the highest and original value of voltage, respectively:0 is the voltage under the lowest humidity measured, and V is the voltage at the highest RH. The influence of the response value of different formulas was discussed in 2 monolayer from APCVD process for a self-powered TENG. The higher specific surface area of nanofiber can achieve better performance in sensor response.In sensor performance discussions, the poly /MXene nanofber TENG proposed by Zhang et al. achieved a 4000% response and response–recovery time of 0.9/6.3 s. The TENG was synthesized using the solution method for poly /MXene preparation and MoSeExcept the electrical humidity nanosensors mentioned above, a special smart deformable TENG was proposed by Chen et al. . The vapIn recent years, the novel material MXene has been applied to the humidity-sensing field ,134,135.The sensing mechanism of MXene can be divided into two parts. First is the chemisorbed and physisorbed layer of water molecules, which is very similar to ceramic- and polymer-based humidity nanosensors and was discussed in Humidity nanosensors were briefly reviewed and categorized into six types, and the nanosensor performance is affected directly by the material, nanostructure and measurement type and calculation formula. Humidity sensors made of ceramics, polymers, semiconductors, and carbon materials have been developed for a long time, and the research is moving forward with more economical manufacturing processes and better sensor performances. On the other hand, humidity sensors made from TENG and MXene materials have emerged in recent years, and have received more attention. Among the above-mentioned various materials, the semiconductor and carbon humidity sensor can achieve better performance. The sensing mechanism of various materials is related to the adsorption of water vapor and photon hopping, but the adsorption of water molecules significantly changes the electrical properties from semiconductor- and carbon-based sensors. Therefore, in addition to resistance and capacitance, the sensing types also include current and voltage types. The current-type humidity nanosensors are able to achieve a higher response due to a larger electrical conduction change. The response value will increase sharply with the measured humidity range, so some research may be able to reach a higher response from a larger RH range. In addition, the response calculation based on the denominator of the highest value or original data directly affects the response value. For instance, the response calculation based on Formulas (11) and (13) shows significant differences, from 100% to 20,000%, in In addition to the traditional applications mentioned in We briefly reviewed the research progress of the fabrication methods, measurement types, sensing mechanisms and applications of humidity nanosensors in recent years. First, the properties and characteristics of different materials, such as ceramics, polymers, semiconductor, carbon-based, TENG, and MXene humidity nanosensors were summarized. In addition, several fabrication processes, such as solution methods, anodization, PVD and CVD, were described under several conditions for sensor preparation. Dozens of studies are listed in several tables for comparison. The semiconductor- and carbon-based nanosensors synthesized with solution methods are expected to achieve better sensor performance, a higher response of >1,000,000% and shorter response–recovery time, below 1 s.The recent advance of improving the sensor performance has been linked to high-specific-surface-area ,59,107 n


Individual animals behave differently from each other. This variability is a component of personality and arises even when genetics and environment are held constant. Discovering the biological mechanisms underlying behavioral variability depends on efficiently measuring individual behavioral bias, a requirement that is facilitated by automated, high-throughput experiments. We compiled a large data set of individual locomotor behavior measures, acquired from over 183,000 fruit flies walking in Y-shaped mazes. With this data set we first conducted a “computational ethology natural history” study to quantify the distribution of individual behavioral biases with unprecedented precision and examine correlations between behavioral measures with high power. We discovered a slight, but highly significant, left-bias in spontaneous locomotor decision-making. We then used the data to evaluate standing hypotheses about biological mechanisms affecting behavioral variability, specifically: the neuromodulator serotonin and its precursor transporter, heterogametic sex, and temperature. We found a variety of significant effects associated with each of these mechanisms that were behavior-dependent. This indicates that the relationship between biological mechanisms and behavioral variability may be highly context dependent. Going forward, automation of behavioral experiments will likely be essential in teasing out the complex causality of individuality. Individual animals exhibit idiosyncratic behavior, even when their genetics and rearing environment are held constant. This variability is termed intragenotypic variability  and likeIncreasing behavioral assay throughput via automation is an effective way to attain the sample sizes needed to study variability. This can be achieved through miniaturization and parallelization of imaging platforms in a lab context . While tDrosophila behavior  of the mean, standard deviation, skewness and kurtosis of the behavioral distributions . These wp-value = 0.04 across 10 experimenters; nor in a model with only genotype and experimental condition as the other predictors: lowest p-value = 0.11). In contrast, 47/569 genotypes have significant effects (p < 0.05) in a linear model where genotype is the sole predictor of turn bias . Incidentally, we noticed the effects of the discreteness of number of turns as a measure, and the resulting limited values that turn bias can take on, as a fractal-like , we were also able to investigate the joint distributions and correlations of these measures. We first tested whether there might be a correlation between turn bias and number of turns, specifically a negative correlation arising from higher sampling error in estimating turn bias for flies making fewer turns. Counter to this prediction, we observed a slight positive correlation  partial rescue by the expression of the “mini-white” allele at non-endogenous transgenic insertion sites (white disruption). White genetic disruption was associated with small reductions in variability in turn bias and turn switchiness , 7.5% (p < 0.001), and 1.8% (n.s.) greater than that of females in turn bias, number of turns, and turn switchiness respectively.It has been hypothesized that individuals of the heterogametic sex will exhibit greater trait variability due to noise in gene compensation , though Lastly, we examined the effect of temperature during behavioral testing, with the hypothesis that flies would exhibit higher variability at high temperature (32–33°C) than at room temperature (22–23°C). This would be consistent with a mechanism in which heat pushes neural circuits out of the range in which physiological buffering keeps circuits operating similarly despite latent developmental and genetic variability . We examwhite gene (which encodes a channel that imports serotonin precursor), sex and temperature. Compared to the effects of genotype and the choice of behavior measure, the effects of these manipulations were generally small and context-dependent, underscoring the complexity of relationships between axes of biological regulation and behavioral variability.We gathered Y-maze data collected by lab members back to the origination of this assay 11 years ago. This large data set comprised the behavioral measures of over 180,000 individual flies that made a total of nearly 80 million left-right choices. With it, we were able to estimate the distribution of three measures of individual behavior with unprecedented precision, even out to the 20th standardized statistical moment . In explAdmittedly, a motivation for this study was the desire to explore a very large data set reflecting the work over many years of many lab colleagues. In that spirit, it is fun to think about how throughput might be expanded another order of magnitude in the coming years. One possibility is robotic fly-handling , which hWhile increasing throughput through further automation is an appealing possibility, and perhaps essential for certain classes of experiments , it is not without conceptual consequences. One of these is how to assess small effects that are extremely statistically significant due to large sample sizes. Two examples from this study are the apparent slight left turn bias  and the With caveats of big data in mind, we want to consider possible errors that might explain the apparent slight (but highly significant) left mean turn bias. All experimenters who conducted these experiments are right-handed. It is formally possible that chiral manipulation during the experimental set-up imparted a slight chirality to turning in the Y-maze, though we cannot think of a convincing mechanism by which this would happen. We also cannot think of mechanisms by which small, inevitable asymmetries in our behavioral rigs would impart a consistent left bias to behaviors measured across several generations of rigs and tracking software versions. Arguments in favor of the apparent left turn bias being real are previous reports of small mean asymmetries in wing size and shape , possiblWhile we found that our data set allowed the precise estimation of the distribution of individual behavioral scores, we also saw that the stability of higher-order moment estimates depended strongly on the behavioral distribution in question . Thus, twhite locus increasing variability in number of turns, disruption of white decreasing variability of turn bias and turn switchiness, males exhibiting slightly lower variability in turn bias but higher variability in number of turns, and conducting experiments at high temperatures lowering variability in number of turns and turn switchiness , the default culture conditions were cornmeal-dextrose media containing tegosept  and incuExperimental flies receiving drug treatments were reared from egg-laying in drug-supplemented media (or control media). Drug media are indicated in the expCond metadata variable see . To supphttps://github.com/de-Bivort-Lab/dblab-schematics/tree/master/Ymaze. Trays were imaged in opaque enclosures constructed from aluminum extrusion and laser-cut acrylic panels . A variety of USB digital cameras (often made by PointGrey) with resolution exceeding 1 MP were used to capture video of behaving flies for real-time tracking at 6–30 Hz. The default assay length was 2 h. Fly centroids were computed in real time using background subtraction implemented in a variety of custom software environments coded in LabView or MATLAB. The centroid tracking software used in recent experiments was MARGO  using custom functions. 95% confidence intervals estimated by bootstrapping were estimated as ± twice the standard deviation of values across bootstrap replicates. For the analysis of the effect of temperature on variability , the 23°The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.BB oversaw the project and conducted data analysis. All authors collected the data and edited the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Scanning tunneling microscopy (STM) is one of the indispensable tools to characterize surface structures, but the distinction between atomic geometry and electronic effects based on the measured tunneling current is not always straightforward. In particular, for single-atomic-thick materials (graphene or boron nitride) on metallic substrates, counterintuitive phenomena such as a larger tunneling current for insulators than for metal and a topography opposite to the atomic geometry are reported. Using first-principles density functional theory calculations combined with analytical modeling, we reveal the critical role of penetrating states of metallic substrates that surpass 2D material states, hindering the measurement of intrinsic 2D materials states and leading to topography inversion. Our finding should be instrumental in the interpretation of STM topographies of atomic-thick materials and in the development of 2D material for (opto)electronic and various quantum applications. Since the tunneling current is determined by both the electronic structure and the geometry of the surfaces, it is not always straightforward to determine true atomic structure of the surface from the apparent topographic image. One of the most commonly studied nontrivialities is the contrast inversion in the topography as a function of tip-to-sample distance, which is observed for various systems: Fe2N surface7, TiO2 surface8, and oxygen adatoms on metal surfaces11. These phenomena are on account of the multiorbital nature of the sample states, as the tunneling current is sensitive to the tails of the decaying wave functions of the sample in vacuum, whose decay rates are orbital dependent.Scanning tunneling microscopy (STM) is a powerful tool that provides the topographic image and electronic structure of surface at atomic resolution. The topographic images obtained by mapping the tunneling current have provided numerous key data to characterize the details of the surface structures, including the surface reconstruction and the charge density wave13, Ir15, and Rh17 substrates and graphene nanomesh on Ir21 and Au24 substrates. STM topography of the periodic corrugations are inverted when the measuring condition changes as reported for STM27 and atomic force microscopy28 of BN on Rh(111) or graphene on Au(111). For BN monolayers, the inversions were explained by the bias-dependent electronic structure26 but in some case29, two inverted images occur simultaneously at the same bias. For graphene on Au(111), the simultaneous appearance of convex and concave topography is interpreted as true geometric corrugation22 based on the similar energy of both structures but it cannot explain the observed dominance of convex geometry.Complications also arise in the topography of geometrically corrugated atomic monolayer on metal surface, such as BN nanomesh on Ru31.In this paper, we reveal that the STM topography of an atomically thick material on a metal surface can be strongly affected by the substrate that the apparent relative height can be severely flattened or even opposite to the true atomic geometry, i.e., a higher location looks lower and vice versa. The phenomenon is quite general for insulators with one-atom thickness, where penetrating substrate states dominate the tunneling current. Unexpectedly, even in graphene, the penetrating substrate states readily supersede the intrinsic electronic structure of graphene, so cautions should be exercised in interpreting the topography data. Quantitative analysis of the penetration explains counterintuitive effects such as the larger tunneling current on the BN monolayer than on graphene and the general topography inversions for atomic monolayers. This suggests that a systematic study of tip-to-sample distance-dependent measurements is imperative for geometrically corrugated monolayers on metal surfaces or lateral heterostructuresFor the STM simulation, we use Cu(111) surface as a noble metal substrate for atomic-thickness materials. The surface is modeled as a slab geometry consisting of 21 layers of Cu thick enough to avoid any artificial interaction between the top and bottom surface states. Our calculations show that at the 32. Cu(111), graphene and BN are selected as prototypical models for metallic substrate, metallic and insulating 2D materials, respectively. Figure Figure mentclass2pt{minimmentclass2pt{minimNext, we investigate the characteristics of the tunneling current as a function of entclass1pt{minimaentclass1pt{minima33 graphene, respectively. The former is still observable at larger LDOS of graphene on Cu(111) at 30 shows a discrepancy between the STM topography and actual atomic geometry31. Our results shed light on why the measured topography of BN looks much higher (> 0.3 Å) in spite that the calculated 2D material-Cu distances  from the following analysis. The wavefunction in the vacuum is entirely determined by its energy and planar boundary conditions. Explicitly, the Bloch wavefunction s and pz for Cu and graphene states, respectively, that xy plane; the smallest Fourier component becomes a Bloch wavevector This is the decay constant of the incident 1D wave with energy 35 and the charge transfer from Cu(111) to graphene tends to decrease the shift. Graphene on Cu presses the top surface states of the substrate, leading to an increase in the energy of the top surface states by 0.13 eV at -1 and are shown in Fig. Figure mentclass2pt{minimmentclass2pt{minim2 may have similar properties with graphene , but due to the thickness of them (3 atomic layers), the dominant contribution of substrate states seems to be not so drastic.We have shown that the Bloch wavevectors of the surface wavefunctions determine the decay rates into the vacuum and that the large Bloch wavevectors of the graphene states cause their electronic structure buried by Cu states. We can assume that this also occurs on other noble metal surfaces or on other crystallographic orientations without surface states such as Cu(100), given that the decay rate is related only to the energy and Bloch wavevectors of the states. In general, the intrinsic electronic structures of 2D materials on a metal substrate can only be observed if they exhibit small planar Bloch wavevectors at the given bias voltage. For graphene, the states between − 7.5 and − 2 eV from the Fermi level are the case, and they are well reflected in the LDOS as demonstrated in Sect. 2 of Supplementary information. Some semiconducting transition metal dichalcogenides such as monolayer H-MoTeWhile the substrates states dominate in the tunneling current, the STM topography resembles the structure of the 2D material on the substrate. This is due to the underlying potential of the 2D material that leads to the in-plane variations of penetrating states from the substrate. Graphene, as an example, generates a spherical potential that is hexagonally arranged at the atomic sites. This potential modulates the penetrating substrate states of Cu, and amplifies them near the potential minimum generated by the 2D material (graphene). This enhancement of the substrate states (black and blue solid lines) is clearly seen in Fig. d increases slowly, and compares them with the actual changes in the distance profile (red dashed lines). Here, the bias voltage is set to be 0.5 V and the topographies (black solid lines) are drawn for five selected current settings corresponding to –5 e/Å3).Next, we discuss the implication for STM measurements of the substrate states that last longer than the 2D material states as they decay into the vacuum. One of the most noticeable consequences of prolonged substrate states is a topography inversion in STM measurements, where the measured topography of a 2D material on a substrate does not correspond to its actual geometric corrugation and even it is reversed, i.e., a peak looks like a valley and vice versa. As an example, Fig. d are mostly negative, which means that the apparent height is even the opposite of the actual atomic geometry. The integrated LDOS around BN comes entirely from Cu states, so the asymmetry of the two peaks, which is the distinguishing feature between Cu states and For graphene, the apparent height variation is much less than the actual geometric variation. For -1), while the relative magnitudes are material specific. We also confirm that topography inversion occurs for Ar monolayer by comparing the integrated LDOS’s of two Ar monolayers (third column) with Finally, we explain the origin of the large tunneling current of an insulator, which is larger than that of a semimetal as graphene at a large distance  or electronically (Moiré pattern) that the corrugation design has been suggested as a new control knob modifying the electronic and chemical properties of parent materials. For those materials, our findings can not only reconcile apparent contradictions between STM topography and atomic structures obtained with first-principles calculations or atomic force microscopy but also help decoupling of intrinsic 2D material properties from the substrate effects.36 and the projector augmented wave method for ionic potentials37 as implemented in the Vienna Ab Initio Simulation Package38. The energy cutoff of planewave basis is 400 eV. Within the Tersoff-Hamann approximation32, STM topographies are obtained by the real space summation of wavefunctions in the specific energy range and the wavefunctions are densely sampled (192 × 192 points for 5.387 Å2 of 2D cell or denser) in the 2D Brillouin zone. For a standard energy cutoff setting of planewave basis (< 600 eV), the exponentially decaying tails of ~ 3 Å above the 2D material are overwritten by numerical noise. To overcome this technical issue, analytical extrapolations are applied to the wavefunctions to achieve accurate LDOS. Details can be found in the Sect. 3 of Supplementary Information.Our calculations and analysis are based on the first-principles density functional theory (DFT) employing the Perdew–Burke–Ernzerhof exchange–correlation functionalSupplementary Information.


The alloy morphology, composition, and microstructure were characterized via scanning electron microscopy (SEM), energy dispersive x-ray spectroscopy (EDS), and X-ray diffraction (XRD), respectively. Subsequently, the CuxNix1− alloy thin films were used to grow graphene in a CH4-Ar-H2 ambient at atmospheric pressure. The underlying rationale is to adjust the CuxNix1− composition to control the graphene. Energy dispersive x-ray spectroscopy (EDS) analysis revealed that a continuous gradient of CuxNix1− (25 at. % < x < 83 at.%) was initially achieved across the 100 mm diameter substrate (~0.9%/mm composition gradient). The XRD spectra confirmed a solid solution was realized and the face-centered cubic lattice parameter varied from ~3.52 to 3.58 x > 69 at.%, bilayer growth dominates from 48 at.% < x < 69 at.%, and multilayer (≥3) growth occurs for x < 48 at.%, where x is the Cu concentration. Finally, a large area of bi-layer graphene was grown via a CuxNix1− catalyst with optimized catalyst composition and growth temperature.We synthesized a combinatorial library of Cu The exceptional properties of graphene such as its good carrier mobility, optical properties (transparent), thermal conductivity, and carrier-density make it an ideal two-dimensional 2-D) material for various electronic, optical, and sensor applications ,2,3. The-D materi2/CH4 ratio in low-pressure CVD processes. Bilayer graphene flakes produced by mechanical exfoliation and transfer process from bulk crystals yield domain sizes which are generally tens of micrometers [Metal catalysts perform an essential role in governing the graphene growth quality, domain size, rate of production, and number of graphene layers ,8,9,10. rometers ; howeverrometers . xNix1− alloy has been reported [xNix1− alloy of appropriate composition one can regulate the graphene layer numbers during CVD graphene growth. Mechanistically it has been shown that the underlying growth mechanism for CVD graphene includes dehydrogenation reaction of the gaseous source of carbon precursors (CH4), diffusion of molecules (substrate), dissolution in CuxNix1− alloy solid solution, and precipitation of carbon atoms (supersaturation) at defect locations such as grain boundaries on the surface (equilibrium graphene growth) upon cooling [Recently, bi-layer and multi-layer CVD graphene growth on Cureported ,19,20,21reported . Hence,  cooling ,23 due txNix1− alloy composition on the number of layers of graphene by thermal CVD process has been studied including bi-layer Cu/Ni and Cu-Ni solid solutions [While there have been numerous studies using CuNi alloys to grow multilayer graphene, large area coverage of high-quality bi-layer graphene remains a challenge ,19,20,21olutions ,19,20,21olutions . AlthougxNix1− alloy range for achieving predominantly monolayer, bilayer, and multilayer (≥3 L) CVD graphene. RF magnetron sputtering of a thin-film CuxNix1− gradient alloy catalyst (~2 μm thick) on Si/SiO2 substrate was performed. Graphene was subsequently grown on the gradient catalyst by an atmospheric pressure CVD process. By employing Raman spectroscopy, we showed that the gradient catalyst is indeed effective at growing a gradient in graphene thickness during a single CVD growth and therefore can serve as a method to rapid prototype recipe/catalyst combinations to grow graphene of controllable thickness. The resultant graphene grown on the gradient catalyst enabled compositions to be identified which yield predominantly monolayer (>85% Cu), bilayer (~61% Cu), and multilayer coverage (<49% Cu). Finally, we demonstrated a final proof-of-concept that combinatorial sputtering can be used to sputter a catalyst alloy of uniform composition which is optimized to produce large-area bilayer graphene. In this study, we experimented with regulating CVD graphene layers and investigating the appropriate at.% of CuxNix1− alloy gradients were grown on silicon dioxide (SiO2)/silicon (Si) substrates via a combinatorial co-sputtering process [−7 Torr and backfilled with Ar to 5 mTorr. The 2-inch diameter targets were mounted and tilted such that when the substrate was not rotated, a composition gradient was achieved across the 100 mm diameter substrate. To achieve an initial composition CuxNix1− gradient of 25 at.% < x < 83 at.%, elemental Cu and Ni targets were first powered to 115 and 200 W, respectively, and sputtered for 66 min to achieve a ~1 μm thick film. A second, Cu-rich sample where 42 at.% < x < 94 at.% was co-sputtered with Cu and Ni sputtering powers 230 and 200 W, respectively, for 89 min to achieve a ~2 μm thick film. Finally, to investigate large-area bilayer graphene growth, a uniform ~2 μm (rotated substrate to eliminate composition gradient) Cu63Ni37 as catalyst film was co-sputtered for 180 min using Cu and Ni powers of 115 and 120 W, respectively. Thin-film (~1–2 µm thick) Cu process ,21,24. TxNix1− composition as a function of position on the substrate was determined via energy-dispersive X-ray spectroscopy (EDS). EDS was measured using an X-ray detector mounted on a Carl Zeiss EVO scanning electron microscope (SEM)  and spectra were obtained at 10 keV and a magnification of 10 kx. A Zeiss Auriga SEM was used to capture high magnification images of the CuxNix1− alloy catalyst before and after graphene growth. The CuxNix1− alloy catalyst. An Empyrean X’Pert3 materials research diffractometer (MRD) was used with the following instrument parameters: wavelength Cu Kα1 (α2 (X-ray diffraction (XRD)  of the as-deposited catalyst was measured at various positions on the substrate to correlate with the crystal structure of the catalyst and confirm the solid solution of the Cu2/Si wafers were placed into the furnace on a quartz pallet. The ends of the CVD tube were sealed by flanges and cooled by convection of ambient air. Graphene growth was achieved by flowing non-flammable stock gas mixtures including 5% CH4 in Ar and 2.5% H2 in Ar. The growth was conducted with the following steps: the first was a flow of 5 L/min of 2.5% H2 in Ar to purge the quartz tube. While flowing 5 L/min of 2.5% H2 in Ar, all three zones of the furnace were ramped to 1000 °C to anneal the catalyst for 1 h. Next, the carbon precursor was introduced by flowing 0.07 L/min and 0.25 L/min CH4 in Ar for all subsequent growths with the co-flow of 5 L/min of 2.5% H2 in Ar. After 30 min growth in the co-flow of 0.25 L/min CH4 in Ar with 5 L/min of 2.5% H2 in Ar, the heating elements were powered down and the furnace was opened to rapidly cool the samples. Co-flow of H2 and CH4 in Ar was maintained during cooling. All CVD experiments were conducted in a 3-zone furnace equipped with a 6” diameter quartz tube. The combinatorial CuNi catalysts sputtered on SiO2/Si wafer. For transfer, the graphene was spin-coated with a mixture of PMMA/anisole at 2000 rpm. The PMMA was cured at 150 °C on a hot plate. The CuNi catalyst was etched away by suspending the PMMA/Graphene/CuNi/Si samples on the surface of an aqueous (NH4)2S2O8 etchant. Surface tension caused the samples to float on the surface of the etchant and the CuNi etch slowly progressed from the edge of the samples. After the CuNi layer was etched away, the Si wafer fell to the bottom of the etching solution and left the PMMA/graphene floating on the surface. The graphene/PMMA was then rinsed in three water baths to remove residual etchant. The graphene/PMMA was then scooped onto a 300 nm SiO2/Si wafer. After water dried from the graphene/Si wafer interface, the sample was submerged in acetone to dissolve the PMMA film and rinse in IPA.Graphene was grown on the CuNi catalyst which was on a SiOThe growth quality of graphene layers is determined by performing Raman spectroscopy measurements. Raman spectra were obtained on a Renishaw inVia Qontor Raman microscope  using a 532 nm laser for excitation. Raman mapping was constructed with Wire V5.0 software. Optical images were collected in a Keyence VHX 7000 series microscope. xNix1− alloy is a function of the composition of CuxNix1− alloy. The focus here was to determine compositions of this alloy that provides the most uniform and coherent single, bilayer, and multilayer graphene growth. The rationale was to adjust the catalyst alloy’s carbon solubility such that the graphene layer number varies at ~1000 °C growth. The combinatorial co-sputtering process in which a wide composition space of the CuxNix1− alloy is obtained is schematically illustrated  orientation  reflection at higher angles—not shown) and, as expected, the diffraction intensity of the as-deposited films were much lower and broader than the annealed films. The two compositions had the expected peak shifts associated with the CuxNix1− solid solution, where higher Cu led to a shift to lower 2θ. After heat treatment, the peaks narrowed, and the intensity grew consistent with the observed recrystallization and grain growth demonstrated in the SEM images. Another peak emerges in the annealed films at approximately 51 degrees, which are consistent with a CuNi-silicide and/or Ni-silicide which result from reactions with the substrate. Comparing the as-deposited and annealed films, there was also a shift to higher 2θ after annealing, which was consistent with either relief of compressive stress in the films. The as-deposited near equiatomic film had a lower intensity in both the as-deposited and annealed film, which was consistent with a decrease in the melting point at a higher Cu in the solid solution. Consistent with this observation, the observed grain size distribution was also slightly higher in the annealed SEM image of the higher Cu content film.To understand how the temperature ramp/soak before graphene growth affects the composition and microstructure of the film, we measured the composition, microstructure, and crystal structure of the film at two compositions as deposited and after the heat treatment (60 min at 1000 °C). Cu) at 1000 °C which is approximately 1 × 10−13 m2/s [x) was given by x = (4Dt)0.5. Thus, during the total high-temperature exposure, this suggested a mean displacement distance of approximately 465 μm. Thus, as realized by our EDS results in As the graphene growth is at a high temperature and the total high-temperature exposure time is on the order of ninety minutes, the question arises if this exposure affects the induced gradient. As an estimate of the diffusion distance, we used the Cu self-diffusion coefficient  and also increased the 5% CH−1, consistent with monolayer graphene. As the Cu concentration is decreased (<69%), multiple peaks emerge, centered at 2668 and 2710 cm−1, which are consistent with the emergence of bilayer and multi-layer graphene. Clearly, the 2D/G ratio  concentration typically results in large variations in graphene thickness and non-uniformity. Furthermore, graphene is often thicker at grain boundaries because these regions have high carbon mobility and results in more graphene precipitation during cooling ,22. As tTo demonstrate the utility of the rapid materials discovery process, we subsequently grew a full wafer with a copper composition of ~63 at.% and transferred the large-area graphene layer to a silicon wafer. A low magnification optical micrograph of a graphene film grown was shown a, left. xNi1-x alloy on Si/SiO2 substrate. This approach produced a thin-film CuxNix1− alloy catalyst with a gradient in composition from 25 at.% < x < 83 at.% across the 100 mm diameter substrate (~0.87%/mm composition gradient). While keeping growth conditions constant, the graphene synthesized on the gradient catalyst ranged from monolayer graphene on the Cu-rich side, to multilayer graphene on the Ni-rich side. A composition of ~61 at.% Cu yielded predominantly bi-layer coverage. A wafer-scale CuxNix1− alloy was grown with uniform ~63 at.% Cu concentration using the co-sputtering technique. This catalyst yielded wafer-scale CVD graphene with >70% coverage being bi-layer graphene. The combinatorial sputtering approach provides a technique to rapidly study alloy catalyst composition for CVD processes. Additionally, the combinatorial sputtering approach can be used to study catalysts for CVD processes beyond graphene, such as carbon nanotubes and hexagonal boron nitride.The number of graphene layers grown in a CVD process is typically tuned by the growth catalyst as well as growth parameters such as temperature, gases, and duration of the growth process. We were able to fine-tune the number of graphene layers produced by combinatorically sputtering a thin film of Cu


However, as the irradiation time increased to 5 min, the friction force increased rapidly with the aging effect and the breakdown of sp3-like bonds between the graphene–substrate interface. This study presents a new method of controlling nanofriction on graphene based on UV irradiation-sensitive posterities in vacuum conditions, which is essential to the application of two-dimensional materials in aerospace equipment, to improve anti-aging properties and wear reduction.The friction characteristics of two-dimensional materials in the ultraviolet (UV) radiation environment are important to the reliability of two-dimensional material nano-structures of space equipment. A novel mechanism of UV light-sensitive nano-friction on graphene was proposed by ultraviolet vacuum irradiation modification using an atomic force microscope (AFM). The surface roughness, adhesion force, and friction of graphene were gradually reduced over a time of irradiation below 3 min. UV185 passes through graphene and causes photochemical reactions between its bottom layer and Si/SiO Electronic components and microelectromechanical systems (MEMS) are important components in the field of aerospace equipment. In the complex space environment, the stability and reliability of their hardware functions are challenged to the extreme. In a vacuum environment, wear and aging cause rapid MEMS actuator failure . GraphenThe frictional properties of functionalized modified graphene have expanded its application in various fields. Among them, after plasma modification and chemical modification, light irradiation modification ,5 is anoIn this paper, the graphene samples were modified via UV vacuum irradiation with different durations. AFM was used to study the effect of UV vacuum irradiation time on the tribological properties of graphene, and to explore the mechanism and method of UV vacuum irradiation regulation on the nanofriction properties of graphene. This study contributes to the further application of graphene in of aerospace field.2) were used as the substrate. First, the silicon substrate was ultrasonically treated for 10~15 min by acetone, ethanol, and deionized water to remove surface impurities, and then placed in a nitrogen environment for natural drying. After that, the graphene was stripped from the single-crystal graphite using a mechanical stripping method and transferred to SiO2/Si substrate, and then transferred to a drying oven at 80 °C for 1 min to weaken the van der Waals force between the graphene sheets and to improve the quality of the mechanical stripping method [2/Si substrate, and the corresponding topography of the red box region measured by AFM tapping mode is shown in In this study, dry oxide N-doped silicon wafers  as the radiation source. The working parameters are shown in An MFP-3D atomic force microscope (AFM) from Asylum Research was used in this study. The probe adopts a silicon probe with a tip of less than 10 nm (Multi75 Al-G Budget Sensors). The normal elastic coefficient of the probe cantilever is calibrated using the Sader thermal noise method, and the lateral sensitivity is determined using the non-contact calibration method. In order to study the effects of UV vacuum irradiation time on the tribological properties of graphene, the experiment must be carried out with the same probe under the same measuring environment conditions .The measurement experiment steps are as follows. First, find and determine the sample area of each experiment in the tapping mode to ensure that each experiment is in the same position. After calibrating the probe normal and lateral sensitivity of probe coefficients in contact mode, the adhesion force, friction–load relationship, and stick–slip characteristics in the 500 nm × 500 nm region were tested to determine the initial tribometric properties of graphene. A UV vacuum irradiation timing experiment was conducted, and an AFM characterization experiment was conducted immediately after irradiation. The specific measurement steps were the same as above. In the adhesive force test, 10 nN loads are taken uniformly, and the adhesive force values are taken from 5 different positions on the same sample and the average value is obtained. The morphology, roughness, adhesion force, friction force, and stick–slip characteristics of graphene under different UV vacuum irradiation durations were compared and analyzed, and the methods and mechanisms of regulating the friction characteristics of graphene were explored.−2 MPa), and modified by irradiation within 2 min in a UV ozone cleaning machine. This study defined the short-term experimental conditions at an ambient temperature of 25 °C ± 2 °C and humidity of 45% ± 5% R.H to pump the tube to vacuum  and scanning speed. The surface of thin graphene shows friction enhancement due to the folding phenomenon [2, forming several suspended bonds, such as hydroxyl and silyl, which increase the surface energy of the substrate and increase with the increase in the treatment time. Moreover, sp3-like bonds are generated in the vertical direction of the interface. Thus, the interface binding between graphene and the substrate is enhanced, and the ability of graphene to deform out of the plane is inhibited, which has a close impact on the surface friction properties of graphene. Other studies have shown that friction force is closely related to the contact mass between two objects, which indicates that friction characteristics can be regulated by changing the contact mass without changing the structural characteristics of the object surface [In order to reveal the effect of UV vacuum irradiation on the bonding strength between graphene and SiOenomenon ,18. Thisenomenon , then th surface ,20.The hydration electron: the REDOX potential is −2.9 V, and φ represents the quantum yield of each reaction .(2)O2→h3 formation is 0.009.In Formula (2), M stands for the remaining energy from other molecules in the environment, and the quantum yield of OGraphene has extremely high resistance to gas permeability and light transmission. So, in UV vacuum irradiation modification, when a portion of UV light is irradiated through graphene on the substrate, the trapped water molecules and oxygen atoms between the interface of the graphene and substrate undergo photochemical reactions (see Formulas (1) and (2)), such as hydrogen bonding, hydroxyl. It is the existence of this part of the hanging key that enhances the interaction between the graphene and basement. To some extent, the folding state of the transferred graphene is slowed down (as shown in Long-time UV vacuum irradiation provides graphene more energy absorption. Under the excitation of high energy, graphene produces partial defects and even layer reduction. As a kind of high-energy ray, ultraviolet ray has enough energy to cut the chemical bonds in most substances. When the ground state of a substance absorbs photons, it forms an excited state, which is followed by photophysical and photochemical reactions, resulting in photoionization, intramolecular rearrangement, free radical formation, and other phenomena . AlthougEtersoff is the potential energy between the graphene substrate and the silicon probe, RV is the repulsion and separation, AV is the attractive part, ijr is the distance between atom i and atom j in the graphene substrate or silicon probe, and ijb is the measure of bond order [LJE is the van der Waals potential between graphene and probe, the potential well depth = 8.0909 meV [ijr is the distance between graphene carbon atom i and probe silicon atom j.Vacuum irradiation is the aging of graphene, that is, the formation of surface defects of graphene after UV vacuum irradiation, as shown in nd order . There i0909 meV , the equThe site where graphene defects are formed has higher activity, which increases the interaction potential between the needle tip and the graphene interface. As a result, the needle tip needs more energy to overcome the high barrier when passing through the defect ,35, resu2 substrate, resulting in hydroxyl, carboxyl, and silanol suspension bonds and sp3-like bonds, which enhances the binding energy of graphene on the substrate, inhibits out-of-plane deformation, reduces the folding effect, and leads to the reduced roughness and improved friction properties of graphene; yet, after long-term UV vacuum irradiation, the surface morphology of the graphene produced an irregular mesh, and the surface was easily destroyed under shear force. At the same time, the surface friction increased obviously with the increase in UV vacuum irradiation time. Because of the strong absorption effect of graphene on UV254, the accumulation of high energy leads to the reduction in the bond energy of the graphene C-C bond until dissociation recombination occurs, resulting in a large number of vacancy defects on the surface of graphene, leading to surface aging and friction increasing.In this study, it was found that the height and surface roughness of thin graphene tended to decrease after short-term UV vacuum irradiation. Then, the friction also decreased with the increase in UV vacuum irradiation time, and the friction enhancement phenomenon in stick–slip behavior was significantly inhibited. The reason is that UV185 passes through graphene and causes photochemical reactions between its bottom layer and Si/SiOThe results of this research present a new method of UV vacuum irradiation modification of graphene. The bidirectional regulation of the tribological properties of graphene was achieved through UV vacuum irradiation. There are more potential oxidation mechanisms under UV irradiation in vacuum/air and more applications of this UV light-sensitive control method in graphene nanofriction as a carrier and lubricant for micro and nanosensors in aerospace equipment.


Airlines’ fleet assignment heavily affects the economic and ecological performance of the global air transportation system (ATS). Consequently, it is inevitable to include potential changes of the fleet assignment when modeling and assessing future global ATS scenarios. Therefore, this article presents a novel explanatory approach to modeling the fleet assignment in the global ATS. The presented approach is based on formulating and solving an optimization problem, which describes the fleet assignment in the ATS through a suitable combination of objective function and constraints. While the objective function combines both the airline and the passenger perspective on the fleet assignment, the constraints include additional operational and technological aspects. In comparison to the available global fleet assignment models in the literature, which rely on statistical approaches, the advantages of the presented approach via an optimization problem lie in the overall scenario capability and the consideration of explicit aircraft types instead of simplifying seat categories. To calibrate and validate our model, we use 10 years of historic flight schedule data. The results underline the strengths and weaknesses of the presented approach and indicate potential for future improvement. Despite the currently ongoing COVID-19 pandemic, the aviation industry is expecting long-term growth of passenger air traffic. Different industry forecasts anticipate reaching pre-pandemic revenue passenger kilometers (RPKs) between 2023 and 2027. For the time span of 2018–2050, the average annual RPK growth rate is estimated to lie between 2.9% and 4.2%; only marginally below pre-pandemic growth estimations. [Contrastingly, the aviation industry and political institutions are aiming to reduce emissions and climate impact of air traffic to accomplish the objectives set by the Paris Agreement –8. HenceHowever, as with all changes to the ATS, airlines are expected to adopt their operations to defend and strengthen their market positioning, when facing potential mitigation measures. One way of adopting an airline’s operations is by updating the fleet and the fleet assignment, deciding which aircraft type is operated on which route. Together with the finite number of airlines operating in the ATS, these individual airline fleet assignment decisions aggregate into changes of the overall fleet assignment in the global ATS. Because these changes of the overall fleet assignment affect the economic and ecological performance of the global ATS, e.g., by changing the location and quantity of emissions or by changing the number of departures and arrivals at airports, it is, thus, inevitable to include potential changes of the fleet assignment when modeling and assessing future global ATS scenarios.Available literature on modeling airlines’ fleet assignments can broadly be categorized into approaches for individual airlines, for multiple competing airlines and for the global fleet assignment. Fleet assignment models for individual airlines strive, e.g., for minimal operating cost or maximal operating profit, while considering operational constraints like limited fleet sizes and airport slot restrictions. Methodically, mathematical optimization approaches like linear programming or mixed-integer programming are used. The early work of Abara  and HaneFleet assignment models for multiple competing airlines focus on capturing the competitive nature of flight scheduling, fleet assignment and flight frequencies when multiple airlines operate within the same market. Methodically, game theory approaches are applied, in which the competition is portrayed as a non-cooperative game between multiple airlines, each trying to maximize its operational profit or market share. While the early work of Hansen  as well Global fleet assignment models are sparsely available in the literature and mainly focus on statistic modeling approaches.The Aviation Integrated Model (AIM) –27 utiliKölker et al.  also useThe dependency between aircraft size, flight distance, and passenger volume on a route is also employed by the Future Air Traffic Estimator (FATE) model . UnfortuSimilarly, the publications about how the fleet assignment in the global ATS is modeled within the Aviation environmental Portfolio Management Tool (APMT) , 31 and In this article, we present a new explanatory approach to modeling the fleet assignment in the global ATS. Similar to the previously discussed approaches for modeling the fleet assignment of an individual airline, our approach is based on formulating and solving an optimization problem. However, rather than optimizing the fleet assignment and flight scheduling of an individual airline, we focus on capturing the dynamics of the global, cross-airline fleet assignment in the ATS. Therefore, we apply a different objective function and a different set of constraints. Our objective function includes the airlines’ and passengers’ perspectives, via direct operating cost (DOC), schedule delay and travel time. Our constraints represent logical, operational, and technological aspects of the fleet assignment, like limited fleet availability, airport capacities, and payload-range capabilities of the different aircraft types. Effects of flight scheduling and airline competition, which are key elements in fleet assignment models for individual airlines and multiple competing airlines, are substitutionally included into our optimization problem via a utilization model and market concentration constraints. In doing so, we achieve a suitable level of detail for modeling the global ATS and ease the computational requirements of our optimization model; leading to computation times of less than ten minutes for the yearly fleet assignment of the global ATS. To calibrate and validate the overall model, we use ten years of historic flight schedule data. In contrast to the statistical modeling approaches for the global fleet assignment, which are available in the literature, our new explanatory approach via an optimization problem has the two main advantages of allowing diverse scenario analyses and including explicit aircraft types instead of aircraft seat categories.The remainder of this article is arranged as follows: Section This section presents first the data sets used for this study; second, the developed overall model architecture; and third, the underlying submodels. Last, the mathematical formulation of the model as an optimization problem is introduced.The Sabre Market Intelligence database provides global historic flight schedule data for the years 2010–2019 . This hiThe Cirium Fleets Analyzer database provides information about the global aircraft fleet for the years 2010–2019 . The fleThe available overall data sets regarding route networks and aircraft fleets are split into two subsets. The first, spanning the years 2010–2016, is used for model calibration and submodel development. The second one, spanning the years 2017–2019, is used for model validation.Aircraft type performance data are taken from the Aircraft Characteristic Manuals (ACMs) provided by the aircraft manufacturers , 37 and Data regarding aircraft-type-specific fuel consumption is derived from EUROCONTROL’s Base of Aircraft Data (BADA) data sets .The required airport-specific information consists of IATA codes, geographic coordinates, number of runways, maximum runway lengths, and the runway configurations. The information is collected and aggregated from various publicly available data sources, e.g., Wikipedia and Google’s satellite imagery.While most of the data mentioned above would be available for a wide variety of different aircraft types, this study focuses on a selected group of relevant passenger aircraft types, which are considered to be representative for the global ATS. The selected 35 Airbus and Boeing aircraft types cover 63% of global departures and 91% of the global available seat kilometers (ASKs) in the historic flight schedule data. A complete list of the selected aircraft types is given on the abscissae of Figs. The overall model structure is displayed in Fig. The fleet assignment model for the global ATS is based on the formulation and solving of an optimization problem. The aim of this optimization problem is to determine which proportion of the offered seat capacity on each route is provided by which aircraft type.The objective function of the optimization problem includes both the airline and the passenger perspective, representing the cost and revenue side of aircraft operation, respectively. The costs are included into the objective function via the DOC per ASK. The revenues are modeled indirectly via the schedule delay and the travel time, both affecting the passenger acceptance of a fleet assignment and therefore the revenue on a route. The weighting between the different aspects of the objective function is determined during the model calibration process, minimizing the differences between calibration data and model results.The constraints of the optimization problem consist of logical, operational and technological constraints. Valid solutions of the optimization problem are required to cover the entire seat capacity of each route. Additionally, the limited availability of each aircraft type in the overall aircraft fleet must be considered. Furthermore, the individual relation between payload and range must be met for each aircraft type. From an infrastructure perspective, the available runway length and runway capacity at each airport must be considered. An additional market concentration constraint takes account of the existence of different airlines with individual fleets and route networks.The solution of the optimization problem results in the primary output of the model: the fleet assignment in the global ATS, answering which aircraft type is used to offer which proportion of the seat capacity on which route. This primary output then leads to additional outputs like the route-specific aircraft mix, the route network of each individual aircraft type and the flight frequency on each route.Several submodels are used as part of the overall model structure. In the following these submodels are introduced and briefly described.The DOC submodel uses the TU Berlin method by J. Thorbeck; also known as the CeRAS DOC model. The TU Berlin method describes the DOC as the sum of the five cost components fuel, maintenance, fees, crew and capital. For each component the method provides an analytic formula for calculating the monetary values in 2010 euro values. , 41reduced flight profiles by simulating aircraft trajectories for all discrete combinations of flight distance (in steps of 100 NM) and load factors  for all aircraft models available in BADA 4. For the simulation standard climb and descent procedures are assumed and corresponding speed schedules are used. During cruise, long-range cruise speed is applied and step climbs are modeled to approximate operationally realistic cruise profiles. We compress the trajectory data (= reduction) by saving the relevant aircraft state parameters  at significant profile vertices only, i.e., transitions between flight phases, into the database, as also, e.g., done by Linke et al. [t on the route  between origin airport o and destination airport d via the quadratic expression given by Eq.  is applied , 43. Thie et al. , 46. Simentclass1pt{minima airport .1\documeo, d) with aircraft type t is calculated in dependency of the great circle distance o, d) with aircraft type t is modeled in dependency of the number of available passenger seats per flight o, d) with aircraft type t, including both flights and turnarounds, is modeled in dependency of the block time The utilization submodel describes the yearly service and transport performance of an aircraft unit. The block time the ACMs , 37 and the ACMs , 48, theThe payload-range relation of each aircraft type is directly derived from the ACMs. For each aircraft type a representative weight variant is selected and the boundaries of the payload-range diagram are modeled as straight lines.t with p passengers on route  are modeled with the analytic formulas given in Eqs.  served by t in a time interval p being the available seats per flight. As shown by Eq.  with aircraft type t in comparison to serving it with the fastest available aircraft type The travel time evaluation for the objective function is based on the relative comparison of block times of different feasible fleet assignments. With Eq. , the addThe historic flight schedule data indicate a negative correlation between the offered seat capacity on a route and the market concentration of the used aircraft types. This correlation is assumed to be caused by the existence of different airlines with individual fleets and route networks in the global ATS. Thus, this effect is inherently not included in the presented methodology due to the used cross-airline approach. Therefore, the market concentration submodel calculates an upper limit  see Eq. . The regThe modeled optimization problem is described by the objective function and constraints in Eqs. –16), th, th16), o and destination airport d is operated by aircraft type t.The aim of the optimization model is to determine a global fleet assignment, which minimizes the cost of serving a route and demand network with an aircraft fleet, Eq. . The fleo, d), the fleet assignment must cover at least the required seat capacity, Eq. , the market concentration of the used aircraft types operated on this route, given by the HHI To describe a valid solution of the optimization problem, the fleet assignment must meet the following constraints: For each route in the global route network, given by the directed tuple of origin and destination airport  must allow an operating range Equations  and 18)18) ensurt to provide the required seat capacity o, d) is the weighted sum of the DOC t to provide the required seat capacity o, d), the required seat capacity t to provide the required seat capacity o, d) is calculated by dividing the required operating time for the route by the available operating time per aircraft unit, Eq.  of origin airport o and destination airport d. Of all possible combinations of airports, only those origin and destination airport tuples  with strict positive flight distance and strict positive offered seat capacity OD, Eq.  of potential combinations of routes and aircraft types. Only those routes and aircraft type combinations are considered, for which the set of the possible number of offered passenger seats o, d), which can be served by the aircraft type t, Eq. , Eq. , an, an15), ptimizer  to solveCurrently, the optimization problem does not include any constraints for the flow of aircraft in the route network, e.g., requiring the number of departures and arrivals being equal per aircraft type and airport. This is because the input data for the route network includes significant sources and sinks regarding the offered seat capacity per airport. Therefore, including constraints on the flow of aircraft would require either manipulating the input data or adding the option of additional empty flights to the optimization problem. Both approaches are considered out of scope for this study.Moreover, the optimization problem also does not require the number of flights on a route to be integer, see Eq. . We rateUsing the previously described methodology and data sets to reproduce the fleet assignment in the global ATS for the years 2010–2019 leads to the following results.Figure Figure To quantify and summarize the previously described differences of the fleet assignments for multiple years and evaluation parameters, a normalized first Wasserstein distance (NWSD) approach is used. For each year the data and model distributions of each aircraft type are compared using the Wasserstein distance and normalized with the mean of the input data. The results of the different aircraft types are then ASK-weighted and averaged for each year. Figure Aggregating and comparing the historic flight schedule fleet assignments and model results for routes with similar distance and capacity characteristics leads to the subsequently described results.Figure Figure Similar to the NWSD analysis performed around Fig. Figure The comparison of the model results with the airport capacity submodel constraints indicates that only seven airports (PEK to LGW) are directly capacity constrained. For all other airports in the overall model the airport capacity submodel calculates a higher runway capacity than required. The capacities of four shown airports are overestimated , two are underestimated (PEK and LHR) and the capacities of the other six shown airports are estimated with only minor differences. For the depicted airports, the largest difference between historic flight schedule data and model results, not caused by too restrictive constraints, occurs for Dubai International (DXB).In the following, the previously described results are discussed and interpreted to draw conclusions for the overall model performance and potential future improvements.First, both the aircraft type results Figs.  and 3 anAdditionally, low ASK categories and aircraft types are more sensitive to potential single outliers, caused by either route or airline characteristics. For instance, all B767-400 passenger aircraft included in the historic flight schedule data for 2016 are operated by two North American network carriers; an information surely affecting the resulting operational scenarios of this aircraft type, but unknown to, and out of scope of the model. Nevertheless, future work should include an in-depth analysis of the low ASK differences.Second, the previously presented results show a relatively low accuracy of the overall model regarding the aircraft type capacity share and the aircraft type market concentration on individual routes, see Figs. Third, the exemplary airport results, see Fig. Additionally, the airport results for DXB underline the incapability of the presented modeling approach to reproduce local characteristics. As the hub of the largest A380 operator, Emirates, an accumulation of A380 flight operations occur at DXB. For the depicted year of 2016 a total of 47% of all A380 operations originate from, or terminate at DXB. As the global cross-airline model has no reason to assume such an accumulation of A380 operations in one airport, other smaller aircraft types are used for flights to and from DXB. Thence, the number of flight operations at DXB is overestimated, as seen in Fig. Finally and despite the above mentioned further improvement potential, the overall accuracy of the results shows that an explanatory approach to modeling the fleet assignment in the global ATS via an optimization problem is very much suitable. When reproducing the average flight frequency and aircraft size for different route categories the NMAEs are smaller than 6%. For the aircraft type distance distributions, the NWSDs between model results and historic flight schedule data are smaller than 12%.In comparison to fleet assignment models in the literature for individual airlines or multiple competing airlines, we made additional assumptions and approximations to allow global fleet assignment modeling. The overall accuracy of the results shows that these assumptions and approximations are reasonable, given the global and more aggregated scope within this study.In comparison to other global fleet assignment models available in the literature, e.g., AIM –27 and FWithin this study, the global aircraft fleet and the global route network with its offered passenger seat capacity per route are constant inputs generated from historic data. Thus, the interactions of passenger demand, fleet assignment, and fleet development are not within the scope of this study. However, for other studies, like the introduction of a new aircraft type in the future , this caWhen using the presented model for future scenarios, this is done under the assumption that the calibration of the submodels and objective function with data from the years 2010–2016 remains valid. This implies that the overall structure of the ATS remains comparable to the calibration time span, e.g., regarding the competition of airlines, the number of available aircraft types and the balance of demand and supply. While there currently is no inherent reason to assume differently, a recalibration to newer data might become necessary in the future. In addition, a sensitivity analysis regarding the calibration time span could give insights into the uncertainty for future scenarios.This article presents a new methodology for modeling the fleet assignment in the global ATS. The new methodology is based on an explanatory cross-airline approach; formulating and solving an optimization problem representing the fleet assignment in the global ATS. The objective function of this optimization problem includes the airline perspective via operating cost and the passenger perspective via schedule delay and travel time. The optimization constraints include logical, operational, and technological aspects of the fleet assignment, like payload-range and airport capacity constraints.The presented results demonstrate the overall suitability of this approach, while also indicating improvement potential for future work. When reproducing the fleet assignment of 35 different aircraft types for the years 2010–2019, the results show errors of less than 6% for the average route flight frequency and aircraft size in comparison to historic flight schedule data. Similarly, the distance distributions of individual aircraft types are reproduced with errors of less than 12%. Larger errors occur for the aircraft type market concentration and capacity share.The accuracy of the overall model can most likely be increased by improving the existing objective function and constraints of the optimization problem. Alternatively, new constraints and additional terms to the objective function should be investigated. Additional aircraft types, in particular regional and commuter aircraft, can improve the applicability of the presented model. Moreover, future work should focus on the application of the presented model. Potential research questions include modeling and analyzing the fleet assignment for future global ATS scenarios as well as deriving operational scenarios for future (green aviation) aircraft designs.


Idiopathic pulmonary fibrosis (IPF) is a serious disease of the lung parenchyma. It has a known polygenetic risk, with at least seventeen regions of the genome implicated to date. Growing evidence suggests linked multimorbidity of IPF with neurodegenerative or affective disorders. However, no study so far has explicitly explored links between IPF, associated genetic risk profiles, and specific brain features.We exploited imaging and genetic data from more than 32,000 participants available through the UK Biobank population-level resource to explore links between IPF genetic risk and imaging-derived brain endophenotypes. We performed a brain-wide imaging-genetics association study between the presence of 17 known IPF risk variants and 1248 multi-modal imaging-derived features, which characterise brain structure and function.17q21.31) and 8 (DEPTOR). Through co-localisation analysis, we confirmed that cortical thickness in the anterior cingulate and more widespread white matter microstructure changes share a single causal variant with IPF at the chromosome 8 locus. Post-hoc preliminary analysis suggested that forced vital capacity may partially mediate the association between the DEPTOR variant and white matter microstructure, but not between the DEPTOR risk variant and cortical thickness.We identified strong associations between cortical morphological features, white matter microstructure and IPF risk loci in chromosomes 17 (DEPTOR expression, unrelated to lung function, and more widespread microstructural white matter changes consistent with hypoxia or neuroinflammation with potential mediation by lung function.Our results reveal the associations between IPF genetic risk and differences in brain structure, for both cortex and white matter. Differences in tissue-specific imaging signatures suggest distinct underlying mechanisms with focal cortical thinning in regions with known high This study was supported by the NIHR Nottingham Biomedical Research Centre and the UK Medical Research Council. Idiopathic pulmonary fibrosis (IPF) is a condition in which the lungs become scarred (fibrosed). Although IPF primarily affects the lungs, co-occurrence with impaired brain function, such as cognitive decline, increased risk of neurodegenerative disorders, cerebrovascular accidents, depression, and anxiety have been reported. The nature of this association is unclear and no post-mortem or in-vivo investigations have been performed to explore this directly. At the genetic level, 17 regions of the genome have been found to drive the genetic risk for IPF.DEPTOR gene locus on chromosome 8, was associated with focal cingulate cortical thinning and more widespread changes in white matter microstructure. The cortical association signature was observed in regions with known DEPTOR expression. The white matter findings may be mediated by forced vital capacity (measure of impaired lung function), hinting to distinct direct (cingulate) and indirect  brain effects of DEPTOR risk alleles.Using previously identified IPF genetic variants and neuroimaging-derived features from 32,431 participants available through the UK Biobank, we performed a brain-wide association study for IPF risk variants. We identified brain endophenotypic associations with two IPF risk associated genetic variants, one in chromosome 17 and one in chromosome 8. In particular, the presence of the IPF risk variant at the The reported brain-wide IPF risk association patterns reveal two potential mechanisms that may explain the known association of IPF and brain disorders: (i) direct and focal, thought to be related to paralimbic mTOR dysregulation and (ii) indirect and widespread, in keeping with secondary effects from impaired lung function, such as hypoxia. Taken together, these data support the hypothesis that genetic risk profiles may explain some of the observed comorbidity of IPF and brain disorders.Idiopathic pulmonary fibrosis (IPF) is a serious chronic lung disease of unknown aetiology affecting around five million people worldwide.,,Although the pathogenesis of IPF is incompletely understood, there is a growing body of evidence that multiple risk factors including ageing, genetic alterations, and environmental factors, such as cigarette smoke exposure, contribute to disease risk. Genome-wide association studies (GWAS) have identified a number of loci associated with increased genetic risk in IPF.,,There has been scattered evidence in the literature for brain-lung relationships, across the spectrum of respiratory disorders.,Neuroimaging can enable unique insight into the nature of the association between IPF and brain dysfunction due to its remarkable sensitivity to subtle structural and functional brain changes. It can reveal topographical and tissue-specific brain signatures linked to genetic risks of primary brain disorders or comorbid brain health in systemic disease. Brain imaging has been successfully used to provide intermediate phenotypes (sometimes referred to as endophenotypes) to assist GWAS studies in brain disorders,In this study we used a brain-wide exploratory approach, capitalising on population-level datasets available through the UK Biobank.,a priori selection criteria. We considered participants that had: i) Neuroimaging (magnetic resonance imaging—MRI) and genetic data; Based on the UK Biobank data released in April 2020, only 41,984 subjects had brain imaging data. ii) Good-quality anatomical MRI (T1-weighted) data; We excluded 2289 subjects because of poor structural imaging data .Neuroimaging and genetic datasets from the population-level UK Biobank resource were used.,1-weighted, T2-weighted, and susceptibility-weighted imaging (SWI)) that provides anatomical and morphological information; ii) Diffusion MRI (dMRI) that reflects white matter microstructural complexity and integrity derived from two models, the diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI); and iii) Functional MRI (fMRI), which indirectly reflects neural activity and cortical connectivity during task-free (resting-state) and task-based conditions. To minimise redundancy in the original set of IDPs, we considered a reduced set of N = 1248 IDPs for each participant, representing all the above modalities and features, but minimising overlap   extracted from all available imaging modalities using a standardised processing pipeline,,As imaging-derived features can be affected by non-biological parameters, we de-confounded them following the recommendations in.,,As suggested in,,−8) with increased genetic risk in IPF .k in IPF , from a We tested the association between each of the 17 IPF variants and each of the 1248 IDPs across 32,431 individuals. For each IPF variant, separate analyses were performed using a general linear model  Body measurements. We used standing height (Data-Field 50), and weight (Data-Field 21002) as covariates. Any missing value in height and weight data was substituted by the imputation  data in the BWAS model to identify brain-wide Imaging-Genetics associations. As suggested in,The BWAS covariates were de-meaned and unit-variance normalised. In total, 25 variables were used as covariates in the BWAS models . When muFor the genetic variants that were found to be associated with brain IDPs, we performed co-localisation analysis using the COLOC methodX), a single mediator (FVC M), and a single dependent variable (brain IDPs Y). This aimed to quantify the direct and indirect relationships between IPF risks, IDPs, and FVC. The direct and indirect standardized effects were calculated using multivariate-adjusted linear regression analysis and a Sobel testTo explore whether associations between neuroimaging features (IDPs) and presence of IPF risk genetic variants, as derived from the BWAS regression model, were indirect effects from subclinical IPF risk manifestation, we undertook mediation analysis focusing on basic lung function that could plausibly affect brain health. We used forced vital capacity , a cumulative measure of lung function ,The statistical software, threshold, sample size, inclusion/exclusion criteria, and methods of each analysis are described in the sections above. All the analysis was conducted using PLINK v2.0,http://www.ukbiobank.ac.uk/ethics/), and these ethical regulations cover the work in this study. Written informed consent was obtained from all participants.No new data were collected from this study. We used publicly-available data from the UK Biobank (under Project 43822). The UK Biobank has approval from the North West Multi-centre Research Ethics Committee (MREC) to obtain and disseminate data and samples from the participants —17q21.31 in chromosome 17) were found to be significantly associated with brain IDPs. The number of associations reaching the Bonferroni-corrected threshold for statistical significance  were 19 and 174 for the chromosome 8 and chromosome 17 IPF variants, respectively  .Fig. 3ThDEPTOR IPF variant that survived Bonferroni correction, along with the sign and magnitude of the association (t-stat). Cortical thickness in the caudal anterior cingulate was negatively associated with IPF risk (lower thickness ↔ presence of IPF risk variant in chromosome 8). A number of white matter (WM) IDPs  and the NODDI intracellular volume fraction (ICVF), and radial diffusivity indices) of major fibre bundles  were also associated with the DEPTOR IPF variant. These IDPs represent WM integrity and microstructure within these bundles. ICVF and FA correlated positively with the presence of IPF risk variant  variant included indices of white matter integrity and microstructure , the NODDI ICVF, and mean diffusivity (MD)), and cortical morphological measures  volumes), with the most significant association corresponding to “cortical surface area of fusiform—right hemisphere (RH)”. No functional measures associated with this IPF variant either. Presence of IPF risk variant in chromosome 17 was associated with increased cortical thinning in certain areas , with lower diffusivities in white matter   variant, where 174 associations with brain IDPs were found . The mode matter , and higl areas) .To explore whether the association of IPF genetic risk with the brain IDPs was likely due to the same causal variant, we performed co-localisation analysis for the IDPs that were found to be significantly associated with IPF variants. Seventeen of the nineteen significant IDP associations for the chromosome 8 IPF locus showed statistical evidence through co-localisation of a likely shared causal variant with the IPF association (posterior ≥ 0.8DEPTOR (chromosome 8) IPF variant and 17 brain IDPs, representing cortical morphology and white matter microstructure. We kept these 17 IDPs for subsequent analysis, indicated by the triangles with bold outlines in Taken together, the above results provide strong evidence—through complementary analyses—of a potentially causal association between the To explore potential mechanisms underlying the identified associations, we conducted a mediation analysis. Given that IPF is a restrictive lung disease characterised by impaired lung capacity, we used forced vital capacity (FVC), which is used for IPF diagnosis and characterisation,DEPTOR IPF variant and the anterior cingulate cortical thickness was not mediated by lung function, as captured by FVC  databaseDEPTOR IPF variant, has a relatively high expression level for the DEPTOR gene. That provides evidence of biological plausibility that the observed association between IPF risk gene variant and IDP changes was detected in a cortical region where the risk gene is indeed strongly expressed.The lack of any mediation through FVC in the case of anterior caudate cortical thickness can hint to a likely direct gene effect that is limited to the anterior caudal cingulate cortex. To corroborate the likely direct nature of this effect, we computed a bulk gene expression map of the DEPTOR variant associations by lung function in keeping with an indirect effect such as systemic hypoxia or inflammation. Conversely, the anterior cingulate thinning was independent of lung function suggesting a direct effect from DEPTOR variant expression in view of the known strong DEPTOR gene expression in the anterior cingulate cortex.We undertook a large brain-wide multi-modal imaging and genetic association study in 32,000 participants of the UK Biobank to establish brain endophenotypes of IPF genetic susceptibility. Strong associations with a likely shared causal variant were found for several of the 1248 studied brain imaging-derived phenotypes (IDPs) for two of the 17 IPF risk associated variants on chromosomes 8 and 17. Co-localisation provided additional evidence that the associated neuroimaging features and IPF share a single causal variant at the chromosome 8 locus. The association pattern consists of focal cingulate cortical thinning and more widespread microstructural alterations in major white matter tracts, such as the corpus callosum and corona radiata. Furthermore, we suggest potential partial mediation of the subcortical IDP–DEPTOR in a very large blood eQTL dataset, a large lung eQTL dataset, and multiple tissues in Genotype-Tissue Expression (GTEx) (including the lung). In addition, it has been also shown that the RP11-760H22.2, TAF2, and KB-1471A8.1 co-localised with the IPF risk signal in GTEx (but not the other larger eQTL databases). Although we would never be able to completely rule out the variants acting through another gene, given the variants were located in DEPTOR, DEPTOR had the strongest evidence for gene expression in relevant tissue and had strong biological supporting evidence,DEPTOR was the most likely gene of interest and was selected as the IPF variant in this locus.Our study builds upon our previous workDEPTOR in chromosome 8 encodes for the Dishevelled, Egl-10 and Pleckstrin (DEP) domain-containing mechanistic target of rapamycin [mTOR]-interacting protein, which is a key modulator  of the mTOR pathways interacting with both of its complexes (mTORC1 and mTORC2),DEPTOR lead to decreased mTOR activity.DEPTOR, major roles have already been established in cancer, metabolism, and immunity explained by its ubiquitous tissue expression and modulation of fundamental cellular processes. In IPF, the recently identified DEPTOR IPF risk allele together with decreased gene expression in lung tissueβ1-mTORC1 axis may hold considerable promise as an anti-fibrotic strategy with the potential to impact multiple IPF pathomechanisms.DEPTOR inhibition was also found in brain tissue in Alzheimer's DiseaseDEPTOR/mTOR ratio is considered to regulate the neuroprotection/neurodegeneration balance in pro-inflammatory states via autophagy regulation. Moreover, DEPTOR central nervous system (CNS) expression in the hypothalamus, circumventricular organs, and autonomic nervous regions support increasing evidence for a further critical role in brain-body homeostatic control.DEPTOR dysregulation through the IPF risk variant is linked with cortical thinning in the anterior cingulate and microstructural changes of major white matter tracts. Moreover, our mediation analysis hints to potential direct DEPTOR and indirect lung phenotypic effects using forced vital capacity (FVC), as an established lung function marker in IPF.DEPTOR bulk gene expression in the Allen Human Brain Atlas.,DEPTOR variant will also reduce DEPTOR expression in areas of high expression, such as the anterior cingulate, resulting in the observed cortical thinning. In turn, the affection of the anterior cingulate may well explain the development of neuropsychiatric comorbidities as evidenced by a large body of neuroimaging studies.Our analysis provides exciting insights that https://open.win.ox.ac.uk/ukbiobank/big40/pheweb33k/variant/8:120934126-A-G). Taken together, hypoxia and chronic neuroinflammation are plausible mechanisms for the observed pattern of subcortical changes in diffusivity.In addition, we propose a second indirect mechanism for the associations found between the chromosome 8 IPF risk variant and more widespread changes in major white matter tracts with potential mediation through FVC. Even if the mediation results were weak, the plausibility of such mechanism cannot be ruled out. Particularly, when considering that the specific characteristics of these imaging associations point to a neuroinflammatory signature that may relate to chronic systemic pro-inflammatory state or tissue hypoxia. High ICVF and reduced diffusivity measures that were correlated with the presence of IPF variant in chromosome 8 correspond to findings in acute neuroinflammation and injuries,DEPTOR risk variant and also overlaps spatially suggesting a similar indirect pathomechanism warranting further mechanistic and mediation studies. Interestingly, the cortical chromosome 17 risk variant signature showed a distinct spatial profile and displayed a dichotomy with cortical thinning but increased cortical surface area. Such a pattern has been reported in a GWAS study, which confirmed the ontogenic dichotomy between thickness and surface area.DEPTOR risk variant). Direct comparisons between our findings and the ones reported inWe detected conspicuously strong effects of IPF genetic risk on brain structure for chromosome 17 that are noteworthy, despite the inconclusive co-localisation analysis. The subcortical endophenotypic pattern of the chromosome 17 IPF risk variant was broadly similar to the microstructural changes seen for the ,,Our results showed no association between IPF genetic risk variants and functional imaging features . This is not surprising and agrees with previous studies,DEPTOR and showed co-localisation with IPF), meaning that the effective degrees of freedom are much lower than 17 .A limitation of our study is that the potential partial mediations did not survive Bonferroni multiple comparison correction, they were only nominally significant. In the mediation analysis, we used an absolute FVC measure (in litres) and accounted for age, sex, height, and ethnicity (our study cohort is restricted to white European ethnic background). We found strong covariance structure between the 17 IDPs used  and chromosome 17 (17q21.31). Through co-localisation a shared causal gene locus was identified for the DEPTOR variant and associated brain signature, and for cingulate cortical thinning no mediation effect from lung function was found. Taken together, these data support the hypothesis that genetic risk profiles may explain some of the observed comorbidity of IPF and brain disorders with further mechanistic studies warranted to characterise additional indirect effects.In summary, we exploited imaging and genetic data from more than 32,000 participants available through the UK Biobank population-level resource to explore links between IPF genetic risk and imaging-derived brain endophenotypes. We identified strong associations between cortical thickness (in the anterior cingulate) and white matter microstructure  with IPF risk loci in chromosome 8 . Our study was performed under UK Biobank Project 43822.All data used in this study are publicly available from the UK Biobank (github.com/Alirezamnk/BWAS_IPF) at the time of publication.Analysis code will be available on GitHub (RGJ reports research funding from AstraZeneca, Biogen, Galecto, GlaxoSmithKline, RedX, Pliant, and Genetech; personal fees from Bristol Myers Squibb, Daewoong, Veracyte, Resolution Therapeutics, RedX, Pliant, Chiesi, Roche, PatientMPower, AstraZeneca, GSK, Boehringer Ingelheim, Galapagos, and Vicore; non-financial support from NuMedii and Action for Pulmonary Fibrosis, outside the submitted work. LVW reports research funding from GSK and Orion Pharma and have conducted consultancy for Galapagos. DPA reports research funding from Biogen. The other authors have no conflict of interest to disclose.