CuNi alloy foils are demonstrated to be one of the best substrates for synthesizing large area single‐crystalline graphene because a very fast growth rate and low nucleation density can be simultaneously achieved. The fast growth rate is understood to be due the abundance of carbon precursor supply, as a result of the high catalytic activity of Ni atoms. However, a theoretical understanding of the low nucleation density remains controversial because it is known that a high carbon precursor concentration on the surface normally leads to a high nucleation density. Here, the graphene nucleation on the CuNi alloy surfaces is systematically explored and it is revealed that: i) carbon atom dissolution into the CuNi alloy passivates the alloy surface, thereby drastically increasing the graphene nucleation barrier; ii) carbon atom diffusion on the CuNi alloy surface is greatly suppressed by the inhomogeneous atomic structure of the surface; and iii) a prominent increase in the rate of carbon diffusion into the bulk occurs when the Ni composition is higher than the percolation threshold. This study reveals the key mechanism for graphene nucleation on CuNi alloy surfaces and provides a guideline for the catalyst design for the synthesis of graphene and other 2D materials. It was observed that the nucleation density of graphene decreases monotonically when the Ni composition in the CuNi alloy increases from 0% to 30%. The decreased nucleation density of graphene on CuNi alloy is attributed to two factors: (i) the passivation of the CuNi alloy surface by both nickel and carbon atoms leads to an increased nucleation barrier; and (ii) the Ni content in the alloy increases the barrier for surface diffusion and changes the diffusion behavior of carbon atoms from surface diffusion to the bulk diffusion when the content of Ni in the alloy is higher than the percolation threshold, 19.9%. In addition, the secondary ion mass spectrometry (SIMS) measurements clearly show that more C atoms can be found in the deep bulk of the CuNi alloy when the Ni content of the alloy is increased, supporting our theoretical prediction.Figure85Ni15 alloy surface has the largest grain size, demonstrating the highest growth rate of graphene on Cu85Ni15. For alloys with a Ni composition less than 15%, the growth rate of graphene increases with the Ni composition. However, for alloys with Ni content larger than 15%, an increase in the Ni composition leads to reduced grain sizes or slower growth rates. Figure 95Ni5, Cu90Ni10, Cu85Ni15, and Cu75Ni25 after 10 min of growth at a temperature of 1050 °C. It can be clearly seen that graphene domains grown on Cu85Ni15 have larger sizes than those grown on Cu95Ni5 or Cu90Ni10, while a further increase of Ni content to 25% (Cu75Ni25) leads to smaller graphene grains. The fast growth of graphene on Cu85Ni15 can be attributed to the efficient decomposition of CH4 on the surface and the extra carbon supply dissolved near the substrate surface, while the slower growth rate at larger Ni content was attributed to the loss of the carbon precursors from the surface through carbon bulk diffusion along the very long Ni chains inside the alloy when the Ni content exceeds the percolation threshold, 19.9%.The grain size and nucleation density of graphene on different CuNi alloy surfaces are shown in 4 flow can also affect the nucleation density of graphene, but the effect is much smaller compared with the composition of CuNi alloy . Moreover, the decrease of graphene nucleation density on CuNi alloy with the increase of Ni composition is not dependent on the carbon flow.In sharp contrast to the growth rate, the nucleation density of graphene keeps decreasing as the Ni composition increases from 0% to 30%  is the energy of a metal substrate with an adsorbed C atom on its surface, E(M) is the energy of the metal substrate, and EG is the energy of a C atom in graphene.The nucleation of graphene starts from the self‐assembly of the carbon precursors  on a substrate surface. To understand the nucleation process, we first calculated the formation energy of a single C atom on CuNi alloy surfaces. The formation energy of a C atom on an alloy surface is defined asFigureThe theoretical calculations above demonstrate that the formation of a graphene sheet on the CuNi alloy surface becomes less favorable at a low carbon concentration when the Ni composition is at some point between 25% and 50%. This is consistent with our experimental observation that no nucleation of graphene was found on the CuNi alloy with Ni content larger than 30%.ω* is the attachment rate of C atoms into a cluster of critical size, Γ = [G*/(3πkTN*2)]1/2 is the Zeldovich factor, N1 is the concentration of C atoms, k is the Boltzmann constant, T is the temperature, and G* is the nucleation energy.ω* = N*edgep(νexp(−Eb/kT)), where N*edge is the number of attachment sites of the 2D nucleus. For a sp2 network structure, N*edge ∼ (6 × N*)1/2, and ν = 1013 s−1. Eb is the barrier for attaching a C atom to the graphene, which can be estimated as the diffusion barrier of a C atom on the alloy surface as the graphene edge on the metal substrate is very active. Thus, we can rewrite Equation According to classical 2D nucleation theory, the nucleation rate of graphene can be written asN1), the diffusion barrier Eb, as well as the nucleation barrier G*.Therefore, under a fixed growth temperature, the nucleation rate is mainly determined by C atom concentration (E(NC) is the formation energy of the carbon cluster on the substrate from a C atom in graphene, Δµ is the chemical potential difference of a carbon atom in the feedstock and in the graphene, and N is the number of carbon atoms in the cluster. To form a graphene nucleus, the carbon clusters need to be greater than the nucleation size, N*, and their formation energy needs to overcome the nucleation barrier, G*. Based on the crystal nucleation theory, the nucleation barrier and nucleation size of the graphene nucleus can be viewed as the maximum point  of the curve of Gibbs free energy (G) versus cluster size (N).[[qv: 9a]]As the graphene nucleation starts from the formation of a carbon cluster, calculating the Gibbs free energy of formation of the carbon cluster is essential to obtain the nucleation barrier of graphene. The Gibbs free energy of formation of the carbon cluster on a metal substrate can be obtained by using the following equation[[qv: 9b]]E(CN), of the graphene nucleus under a fixed chemical potential difference Δµ can be obtained by[[qv: 9b]]E0 is a term of constant for a specified metal, it can be regarded as the energy difference between sp2 hybridized C in a perfect graphene and in the carbon cluster; Eedge is the formation energy of each edge atom, and √(6N) represents the number of edge atoms.The formation energy, E(CN) in Equation By replacing N*, and the nucleation barrier, G*, can be obtained by assuming dG/dN = 0, and thusThe nucleation size, G*, on the metal surface is determined by Eedge under a fixed chemical potential Δµ. According to Equation Eedge can be obtained by calculating the formation energy of a carbon cluster. Based on this argument, the nucleation of graphene on different CuNi alloy surface can be compared by calculating the formation energy of a carbon cluster.Therefore, the nucleation barrier, 24, is selected. The formation energy of C24 on a transition metal surface can be calculated by using a similar definition to that of the single atom given by Equation E(C24@M) is the energy of a C24 cluster on a metal substrate.In this study, a typical carbon cluster, C24 cluster on the four pure alloy surfaces  surface is 13.16 eV, and it decreases to 12.81 eV when the Ni composition of CuNi alloy is 11%. Then, formation energy is further decreased to 11.39 eV when the Ni content is increased to 25%, as shown by the black line in Figure 24. Such an increased reactivity of the alloy surface caused by Ni doping has also been reported in previous studies.Constrained by the size of the theoretical model, only Cu100) and three CuNi(100) surfaces with Ni composition of 11%, 16%, and 25% are considered in our calculation. We first studied the formation of a Ces Figure3a. The0 and thr24 on CuNi alloy surfaces by taking the effect of dissolved carbon atoms into account. In our calculations, the effect of carbon solubility in CuNi alloy is simulated by adding some additional carbon atoms in the most energetically stable adsorption sites on the alloy surfaces. As shown in Figure 24 on the CuNi/C surface were found to be greatly increased  surface (13.16 eV).However, it should be noted that a higher Ni content in the CuNi alloy normally leads to a faster dissolution of carbon atoms and a higher binding energy of the dissociated carbon atoms. Although the dissolution of carbon atoms in Cu is negligible, their solubility in Ni is much higher.d Figure c. For ex24 increases from 13.16 to 15.26 eV, demonstrating that the doping of Ni content into the Cu bulk could passivate the Cu(100) surface. Another contribution to the surface passivation stems from the dissolved carbon atoms, for example, the formation energy of C24 on the (100) surface of Ni2C is 6.31 eV higher than that on the Ni(100) surface. This means carbon dissolution can considerably passivate the Ni atoms.The increase of the formation energy can be understood by considering the passivation of the CuNi alloy surface atoms from both the Ni atoms and the dissolved carbon atoms. As shown in Figure S2 (Supporting Information), by replacing the sublayer Cu atoms with Ni atoms, the formation energy of C24 on the CuNi/C surfaces show a tendency to first rise and then decrease, and thus have a peak at a Ni composition of ≈15%. This demonstrates that the passivation of the CuNi alloy surface has a maximum at a Ni composition of ≈15%, further increase of Ni content makes the alloy surface less passivated.The calculations clearly demonstrate that carbon dissolution into the CuNi alloy can effectively passivate the alloy surface, leading to greatly increased graphene nucleation barriers. It is interesting that the formation energies of C75Ni25 is lower than that on the Cu85Ni15 surface because the former has a lower nucleation barrier than the latter one.Above calculation explains very well the decreased nucleation density of graphene on CuNi alloys with Ni composition varying from 0% to 15% because a high nucleation barrier always contributes to a low nucleation density. However, it cannot explain why the nucleation density on Cu87.5Ni12.5 and Cu75Ni25 to represent typical alloys without and with a very long chain of adjacent Ni atoms penetrating into the bulk, respectively. As the percolation threshold of a face centered cubic (fcc) material is ≈19.9%,87.5Ni12.5, and alloys with Ni composition larger than 19.9% can be represented by that of Cu75Ni25.According to Equation 87.5Ni12.5 is shown by the blue line in Figure87.5Ni12.5, the calculated highest diffusion barrier is 2.83 eV , while the bulk diffusion has a barrier of 1.43 eV only. This demonstrates that the decomposed carbon atoms on the alloy surface can relatively quickly diffuse into the bulk of the alloy, which will greatly reduce the concentration of the carbon precursor on the surface. Therefore, although the higher Ni composition leads to a relatively fast feedstock decomposition and thus high C concentration,Although the surface diffusion is very similar, the carbon diffusion into CuFigure19–1023 atoms cm−3 near the surface of the alloy and this rises sharply with an increase in the relative Ni content. The C atom concentration drops greatly in the bulk of the alloy, indicating that the system is far from thermal equilibrium and the carbon atom concentration inside the bulk was not saturated when the experiments were carried out. In Cu95Ni5, the C concentration is as low as 1019 atoms cm−3. Considering that there are ≈1023 atoms cm−3 in the material, the proportion of the dissolved C in the bulk of Cu95Ni5 is as low as 10−4. This demonstrates that relatively few C atoms have diffused from the surface to the bulk. However, the dissolved C in the bulk of Cu75Ni25 alloy is ≈10 times higher, demonstrating the faster diffusion of C atoms from the surface to the bulk in this case. The SIMS images of depth profile clearly show that the C dissolution in the Cu75Ni25 alloy is much higher (with more bright points) than those in other alloys.The time of flight (TOF)‐SIMS depth profile analysis is performed to study the C dissolution in CuNi alloys under the same condition of graphene growth procedure. As shown in 75Ni25 surface could be lower than that on the Cu85Ni15. Although graphene on Cu75Ni25 has lower nucleation barrier and the feedstock can be dissociated faster, the number of C atoms involved in the graphene nucleation is less than that on the Cu85Ni15 surface because some of the dissociated C atoms diffuse quickly into the bulk. This greatly decreases the graphene nucleation rate and thus leads to a lower nucleation density at high Ni content.Combined the theoretical calculations with the experimental observations, we conclude that the surface diffusion of C atoms dominates for the CuNi alloy with low Ni composition (Ni% < 19.9%) while bulk diffusion of C atoms becomes dominant when the Ni composition of the alloy is higher than the percolation threshold, (Ni% > 19.9%). Based on this argument, we can explain why the nucleation density of graphene on CuIn summary, against our intuition, we have found that the nucleation density of graphene on CuNi alloy surface always decreases with an increase in the Ni content, although systems with a high Ni content have a fast carbon feedstock decomposition. Such a “contradictory” phenomenon has been explained by our theoretical calculations. For CuNi alloy with low Ni composition (<19.9%), the decreased nucleation density can be attributed to the greatly increased nucleation barrier caused by carbon and Ni passivation to the Cu surface, together with the increased diffusion barrier of carbon atoms on the alloy surface. For CuNi alloy with high Ni composition (>19.9%), in addition to the two factors mentioned above, the fast diffusion of surface C atoms into the bulk through the continuous Ni chains in the alloy results in a rapid loss of the surface atoms and which greatly decreases the nucleation density.Experimental Details—Preparation of CuNi Binary Substrate: A 6 cm × 6 cm Cu foil  was first electrochemically polished with a current density of ≈0.3 A cm−2 for 90 s, then annealed at 1050 °C in a mixture of Ar/H2 (400/100 sccm) for 2 h followed by electroplated with a current density of ≈0.01 A cm−2. A Ni film with a certain thickness was deposited on the Cu foil at a rate of 200 nm min−1. The polishing solution was a mixture of 500 mL of water, 250 mL of ethanol, 250 mL of orthophosphoric acid, 50 mL of isopropyl alcohol, and 5 g of urea. The electrolytic solution consisted of 1 L of water, 280 g of NiSO4⋅6H2O, 8 g of NiCl2⋅6H2O, 4 g of NaF, and 30 g of H3BO3.Experimental Details—Characterization of CuNi Surface: X‐ray diffraction (XRD) and electron backscattered diffraction (EBSD) are employed to analyze the crystallinity and surface morphology of metal substrate. The XRD spectra shows that grains in the CuNi foil have a wide range of crystallographic orientations before annealing but a strong (100) texture is developed after high temperature annealing . It could also be observed that Cu and Ni were completely interdiffused to each other to form a uniform CuNi alloy. However, the density of the grain boundaries did not increase obviously with the increasing Ni content. The EBSD image showed that the average grain size on Cu95Ni5, Cu85Ni15 and Cu75Ni25 surfaces was ≈200–500 µm . Longer pregrowth annealing under Ar/H2 flow could help in the production of CuNi (100) facets while mitigating surface roughness and grain boundary (GB) migration. The uniform orientation of CuNi alloy after polishing and annealing is helpful to improve the nucleation density and quality of single crystalline graphene.Experimental Details—Graphene Growth: CuNi alloy foils containing 5–15 at% Ni  were used as substrates. Prior to growth, the CuNi foils were cleaned by hydrochloric acid, acetone, isopropanol, and deionized water to remove the surface oxide and organic impurity. The CuNi substrate was then loaded into a quartz tube with a diameter of 50 mm. The substrates were annealed at 1050 °C 2 h in a H2 and Ar flow (H2/Ar = 50/1000 sccm) at atmosphere pressure to further increase its surface flatness and grain size. Then, 80–120 sccm methane (0.5% CH4 diluted in Ar), 15 sccm H2, and 300 sccm Ar were fed into the CVD system. Millimeter‐sized graphene domains could be obtained even after exposure to methane for only about ≈5–10 min. After that, the substrates cooled down to room temperature in a mixed Ar/H2 flow.Experimental Details—SIMS Measurements: SIMS can provide detailed chemical information of the surface and subsurface of a material with high‐accuracy depth profiling. In this study, TOF‐SIMS (ION‐TOF GmbH TOF.SIMS5) depth profile analysis was performed to further test the component of alloys and the carbon dissolution in CuNi alloys. It could be observed that Cu and Ni were completely interdiffused to each other to form CuNi alloy foils with the atomic proportions determined by the amount of the deposited Ni film at high temperature annealing. The SIMS measurement provided more decisive information to understand the local nucleation feature and growth conditions on CuNi alloy. Surface enrichment of carbon was detected on the CuNi alloy after the substrate was exposed to methane for few minutes. The surface concentration of carbon reached 1 × 1022–1 × 1023 atoms cm−3 and raised with the increasing of the proportion of Ni content  slab and a vacuum layer of 15 Å were used along the z direction . Also, a four‐layer Ni (or Cu) slab with the same vacuum was used to calculate the formation energy of C24 on the substrate.2 of the PBE functional.k‐points selected. A plane‐wave basis kinetic energy cut‐off of 400 eV was used in all the calculations.Density functional theory (DFT) calculations were performed using the Vienna Ab initio Simulation Package,The authors declare no conflict of interest.SupplementaryClick here for additional data file.


Here, we report on the observation and manipulation of individual magnetic moments in graphene open-shell nanostructures on a gold surface. Using scanning tunneling spectroscopy, we detect the presence of single electron spins localized around certain zigzag sites of the carbon backbone via the Kondo effect. We find near-by spins coupled into a singlet ground state and quantify their exchange interaction via singlet-triplet inelastic electron excitations. Theoretical simulations picture how electron correlations result in spin-polarized radical states with the experimentally observed spatial distributions. Extra hydrogen atoms bound to radical sites quench their magnetic moment and switch the spin of the nanostructure in half-integer amounts. Our work demonstrates the intrinsic π-paramagnetism of graphene nanostructures.Turning graphene magnetic is a promising challenge to make it an active material for spintronics. Predictions state that graphene structures with specific shapes can spontaneously develop magnetism driven by Coulomb repulsion of  π-magnetism in graphene systems has been predicted but remains an experimental challenge. Here the authors report the discovery of unpaired electron spins localized in certain sites of graphene nanoribbons, and the measurement of their coupling by inducing singlet-triplet excitations with a scanning tunneling microscope. Generally, magnetism is associated to a large degree of electron localization and strong spin–orbit interaction. Both premises are absent in graphene, a strongly diamagnetic material. The simplest method to induce magnetism in graphene is to create an imbalance in the number of carbon atoms in each of the two sublattices, what, according to the Lieb’s theorem for bipartite lattices2, causes a spin imbalance in the system. This can be done by either inserting defects that remove a single pz orbital6 or by shaping graphene with zigzag (ZZ) edges8. However, magnetism can also emerge in graphene nanostructures where Lieb’s theorem does not apply10. For example, in π-conjugated systems with small band gaps, Coulomb repulsion between valence electrons forces the electronic system to reorganize into open-shell configurations11 with unpaired electrons  localized at different atomic sites. Although the net magnetization of the nanostructures may be zero, each radical state hosts a magnetic moment of size μB, the Bohr magneton, turning the graphene nanostructure paramagnetic. This basic principle predicts, for example, the emergence of edge magnetization originating from zero-energy modes in sufficiently wide ZZ14 and chiral16 graphene nanoribbons (chGNRs).Among the many applications predicted for graphene, its use as a source of magnetism is the most unexpected one, and an attractive challenge for its active role in spintronic devices19 hide the weak paramagnetism of radical sites20. Scanning probe microscopies can spatially localize the source states of magnetism19, but they require both atomic-scale resolution and spin-sensitive measurements. Here we achieve these conditions to demonstrate that atomically defined graphene nanostructures can host localized spins at specific sites and give rise to the Kondo effect22, a many-body phenomenon caused by the interaction between a localized spin and free conduction electrons in its proximity. Using a low-temperature scanning tunneling microscope (STM) we use this signal to map the spin localization within the nanostructure and to detect spin–spin interactions.The experimental observation of spontaneous magnetization driven by electron correlations is still challenging, because, for example, atomic defects and metal impurities in the graphene structures23. We deposited the organic molecular precursors 2,2′-dibromo-9,9′-bianthracene  surface by cross-dehydrogenative coupling of adjacent chiral GNRs (chGNRs)ene Fig.  on a cleene Fig.  to produ11 surfacIn Fig. I/dV) on the three types of junctions. Spectra on the bright sites of Type 1 and 2 junctions show very pronounced zero-bias peaks 34. The more pronounced signal for either particle tunneling (over ZZ) or for hole tunneling (over PC) indicates the spins system lies out of particle-hole symmetry point, with EF closer to the corresponding singly unoccupied or singly occupied (SO) state, respectively. Hence, the gap between dI/dV peaks in Fig. Junctions with two bright regions (Type 3) show different low-energy features: two peaked steps in dmeV Fig. . The ste31, one obtains the exchange coupling J = 2.7 (9.9) meV for the upper (lower) spectrum.Interestingly, the spectral gap in Type 3 junctions increases with the length of the connecting ribbons. In Fig. I/dV signal measured in Type 3 junctions. This supports that this signal is an intrinsic effect of junction edge sites, rather than caused by defects or other atomic species.To explain the emergence of localized spins, we simulated the spin-polarized electronic structure of chGNR junctions using both density functional theory (DFT) and mean-field Hubbard (MFH) models (see Supplementary Notes π-electrons as described within a tight-binding (TB) model  and lowest unoccupied (LU) molecular states of the nanostructure. Due to the large degree of localization , and exchange coupled as schematically illustrated in Fig. π-sextet rule to the close-shell structure of Fig. The origin of the spontaneous magnetization can be rationalized by considering the effect of Coulomb correlations between del Fig. . The spidel Fig. . These aI/dV spectra found over Type 3 junctions  substrate has the general trend of hole-doping GNRs43, which in some systems caused depopulation of the mid-gap states37. The band structure of the  chGNR and, in particular, the proximity of the VB to EF, is a crucial aspect to stabilize the electron population of the GNR junction on the surface and hence, for the survival of their magnetic ground state.Open shell configurations of extended + ion sputtering and subsequent annealing to 730 K. The molecular precursor  was sublimated at 170 °C from a Knudsen cell onto the clean Au(111) substrate kept at room temperature. Then the sample was first annealed at 200 °C for 15 min in order to induce the polymerization of the molecular precursors by Ullmann coupling, then the sample was annealed at 250 °C for 5 min to trigger the cyclodehydrogenation to form chiral graphene nanoribbons (chGNRs). A last step annealing at 350 °C for 1 min created nanostructure junctions. A tungsten tip functionalized with a CO molecule was used for high-resolution images. All the images in the manuscript were acquired in constant height mode, at very small voltages, and junction resistances of typically 20 MΩ. The dI/dV signal was recorded using a lock-in amplifier with a bias modulation of Vrms = 0.1 mV  operated at 1.2 K with a magnetic field up to 3 T was used to measure the temperature and magnetic field dependence of the Kondo resonance, while other experiments were done with a home made STM operating at 5 K. Both setups allow in situ sample preparation and transfer into the STM. The Au(111) substrate was cleaned in UHV by repeated cycles of Ne mV Fig.  and Vrms44 of DFT. Exchange and correlation (XC) were included within either the local (spin) density approximation (LDA)45 or the generalized gradient approximation (GGA)46. We used a 400 Ry cutoff for the real-space grid integrations and a double-zeta plus polarization (DZP) basis set generated with an 0.02 Ry energy shift for the cutoff radii. The molecules, represented with periodic unit cells, were separated by a vacuum of at least 10 Å in any direction. The electronic density was converged to a stringent criterion of 105. The force tolerance was set to 0.002 eV/Å. To complement the DFT simulations described above we also performed simulations based on the MFH model, known to provide a good description for carbon π-electron systems48.We performed calculations with the SIESTA implementationSupplementary InformationPeer Review File


These findings show that atomic-scale control of hydrogen adsorption on graphene can give rise to a robust magnetic order.The origin of the ferromagnetism in metal-free graphitic materials has been a decade-old puzzle. The possibility of long-range magnetic order in graphene has been recently questioned by the experimental findings that point defects in graphene, such as fluorine adatoms and vacancies, lead to defect-induced paramagnetism but no magnetic ordering down to 2 K. It remains controversial whether collective magnetic order in graphene can emerge from point defects at finite temperatures. This work provides a new framework for understanding the ferromagnetism in hydrogenated graphene, highlighting the key contribution of the spin-polarized pseudospin as a “mediator” of long-range magnetic interactions in graphene. Using first-principles calculations of hydrogenated graphene, we found that the unique ‘zero-energy’ position of H-induced quasilocalized states enables notable spin polarization of the graphene’s sublattice pseudospin. The pseudospin-mediated magnetic interactions between the H-induced magnetic moments stabilize the two-dimensional ferromagnetic ordering with Curie temperatures of T Several experiments showed that the ferromagnetic order in graphitic materials originates from the carbon π-electron systems rather than from magnetic impurities15. Further studies have shown that point defects in graphitic materials contribute to carbon-based ferromagnetism21. Recently, proximity-induced ferromagnetism25 was also demonstrated for a single-layer graphene placed on an insulating magnetic substrate. Despite decades of research on carbon-based magnetism, it remains unclear under what conditions long-range magnetic order can emerge from point defects in graphitic materials. Contrary to the previous notion that graphene ferromagnetism arises from defect-induced magnetic moments21, Nair et al.5 recently demonstrated that point defects in graphene, such as fluorine adatoms and vacancies, lead to notable paramagnetism but no magnetic ordering down to liquid helium temperatures. The maximum response of the induced paramagnetism was limited to one moment per approximately 1,000 carbon atoms. The lack of collective magnetic order in graphene was attributed to the absence of long-range magnetic interactions between the well-separated magnetic moments5, with the implication that previously reported room-temperature ferromagnetism in graphitic materials27 might originate from undetected magnetic impurities or particles.The two-dimensional (2D) magnetism in graphene has attracted considerable attention because of its exceptional promise in graphene-based spintronics11 have provided direct evidence that individual hydrogen atoms adsorbed on graphene induce magnetic moments, creating opportunities for atomic-scale control of graphene ferromagnetism12. As in the graphene systems with fluorine adatoms or carbon vacancies5, however, the magnetic response in hydrogenated graphene is limited because the phase separation into pure graphene and fully hydrogenated graphene  parts is thermodynamically more stable28. The ferromagnetism at reasonably high temperatures in such a magnetically dilute system thus requires long-range magnetic interactions between the H-induced magnetic moments, as well as controlled hydrogenation of graphene under non-equilibrium conditions.Recent scanning tunneling microscope experimentsσ-bond formation effectively induces a “vacancy” in the π-electron system31, creating a quasilocalized state occupied by an electron33 and an associated magnetic moment  calculations, we show that hydrogenated graphene not only hosts H-induced localized spins but also responds to them by forming spin-polarized pseudospin as a “mediator” of long-range magnetic interactions in graphene. In hydrogenated graphene, it is well-known that the C–H ent Fig. . The H-iDR, which arises from the overlap between the wavefunctions of “vacancies”, leads to ferromagnetic (FM) interaction between “vacancies” in the same sublattices18. Because of the slow decay of the wavefunctions, the interaction range is relatively large  In addition to the “conventional” magnetic interactions based on the wavefunction overlap, we show that “unconventional” long-range interactions exist that involve a delocalized “mediator” between the localized spins  interaction is FM for a pair of “vacancies” in the same sublattice Figure rge Fig. . For tworge Fig. , the eleTo distinguish the long-range “mediated” magnetic interaction from the direct exchange interaction, we performed DFT calculations of hydrogenated graphene using judiciously chosen H-adatom positions  in the first term denotes the H pairs of a given type, and i on the A sublattice can take either 1 (spin up) or –1 (down). The spin-polarized DFT calculations were performed in the Sz = S subspace; thus, m(1)} and {m(2)}, for which some of the FM-coupled  pairs in {m(1)} were changed to the AFM-coupled  pairs in {m(2)} (Supplementary Table Nflip), the energy difference is given by where the symbol , with an energy gain proportional to Figure A and HB in graphene. The spin ground state has 18, where |HA| and |HB| are the number of H adatoms at the graphene’s A and B sublattices. Assuming an antiparallel net magnetization on opposite sublattices at finite temperatures, the low-energy graphene state for the occupied electrons is characterized by the spin-up t* = 3.3 eV in the DFT calculations 〉 and 〈mB(T)〉, at temperature T are calculated self-consistently (Methods). We define two characteristic temperatures, B is the Boltzmann constant, and nH is the atom percentage of H adatoms. Figure F for the different probabilities, PA and PB, of having HA and HB on the graphene layer. The magnetization per H adatom is 35. A slight imbalance with PA = 0.51 and PB = 0.49, for example, induces m = 0.02 μB/atom, which corresponds to the magnetization per weight of 0.1 Am2/kg at nH = 1 at. % β has the critical exponent β = 0.5, regardless of PA and PB , the Tc is determined by competition between the FM and AFM interactions38, i.e., A through the direct exchange interaction, and the same SPPS simultaneously stabilizes the spin-down HB through the superexchange interaction. The FM and AFM interactions thus “cooperate”, rather than compete, in hydrogenated graphene.We note that the AFM interaction contributes to  compete ; the spiH ~ 1 at. %. The “cooperativeness” of the FM and AFM interactions contributes to the high Tc. The realization of the robust 2D ferromagnetism in such a magnetically and electrically dilute system is unusual; the 2D magnetism in hydrogenated graphene is described by mean-field theory owing to the intrinsic long-range nature of the SPPS-mediated interactions. Our new finding of the mechanism underlying graphene ferromagnetism should have enormous implications for understanding and atomic-scale control of graphene-based magnetism, which is an important step toward bringing the vision described in “Painting magnetism on a canvas of graphene (ref.12)” closer to reality.In summary, we have shown that the SPPS-mediated, long-range magnetic interactions give rise to a robust magnetic order in hydrogenated graphene, which can be stable even at room temperature for the H concentration of n39) to DFT, as implemented in the Vienna ab-initio Simulation Package40. The DFT calculations employed the projector augmented wave method42 with an energy cutoff of 500 eV for the plane-wave part of the wave function. For the Brillouin zone integration, we used a Γ-centred grid containing enough k points, as dense as the 60 × 60 grid for the two-atom unitcell of graphene.We calculated total energies and spin densities of partially hydrogenated graphene using the generalized gradient approximation  or the number of the C atoms in a supercell with |Λ| = 96, 150, 216, 300, 384, 432, and 864 } =  and {m(2)} = , were considered. Then, from Eq. To extract the interaction strength of the long-range AFM interaction B is the Boltzmann constant, and nH is the H concentration, A = PB, A ≠ PB, it gradually increases in magnitude with increasing T. From the condition of no net magnetization on each sublattice at Tc, we obtained We used a mean-field approximation with Supplementary Information


Graphene is considered a promising substance in applications related to the capture and reduction of the environmental impact of fluorinated gases. However, further research is still required to explore all related possibilities. In this work, the potential use in this context of nanofluids (NFs), obtained by dispersing graphene nanosheets in fluorinated ionic liquids (FILs) is investigated. As a starting step, a thermal and structural characterization for this type of IoNanofluids (IoNFs) is presented. The highly nanostructured nature of FILs has been recently demonstrated. The presence of fluorinated moieties is responsible for enhancing the accommodation of solutes such as small gases. The strong tendency to self-assemble forming continuous and supramolecular structures, and the versatility to rearrange in several conformational features allows the stabilization of nano colloidal systems. It is essential to perform a comprehensive study of their structural features to understand the behavior of this type of heterogeneous systems. Therefore, we present screening on the phase and structural behavior of these novel IoNFs to discover and develop optimized systems where FILs turn out to be advantageous. Thermogravimetric analysis (TGA) was employed to evaluate IoNFs mass losses with temperature, and their solid–fluid phase transitions were located using a differential scanning calorimeter (DSC). Their rheological properties were also determined through oscillatory experiments, obtaining the viscous and loss moduli. In addition, the structural percolation transition was also identified. During the last decades, intensive research has been performed concerning ionic liquids (ILs) because they have been identified as environmentally friendly solvents in a wide range of applications , includiOn the other hand, graphene nanosheets have been recently employed in the formulation of nanofluids (NFs). A survey of the literature reveals that this application of graphene in NF formulation is finding application, for instance, in heat transfer processes and energy production optimization ,10,11, bIn particular, fluorinated ILs (FILs) are characterized by a higher molecular rigid structure and lower polarity as a result of the nature of carbon−fluorine bonds that promotes the formation of a nano segregated structure . Their u2, and their emissions have increased by 60% since 1990.In this work, we propose progress in the design of new absorbent media for the separation and capture of fluorinated gases (F-Gas). The atmospheric concentration of greenhouse gases (GHG) increases every year, contributing to the increases observed in the global temperature. In fact, most F gases have huge global warming potentials, some of them reaching 23,000 times the value of COF-Gas are used as an alternative to chlorofluorocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs) to prevent ozone depletion. In the literature, F-Gas have been shown to have high solubility in FILs, and thus, these innovative solvents have been proposed as separation and capture agents for these environmentally harmful gases. The combination of FILs with graphene nanosheets is proposed as a new step forward in this application. This tailoring of these innovative IoNFs is intended to enhance gas solubility through the selective adsorption of these gases on the surface of the nanosheets, which can be modified through chemical functionalization to increase affinity with F-Gas molecules. Nevertheless, the thermophysical and phase equilibrium transitions of these IoNFs are still poorly understood, and demand a rigorous characterization before considering them for the proposed application.The use of IoNFs in this application presents some potential advantages if compared with traditional NFs. We can cite, for instance, the improved nanofluid stability due to the FIL viscoelastic nature, which reduces agglomeration and settling. In addition, and due to the IL negligible vapor pressure, no concentration drifts are produced due to the solvent evaporation during sonication, and this feature also improves the traditional NF performance.With this objective in mind, the characterization of graphene-derived NFs will be performed using a highly surfactant FIL as a base fluid . Prelimi2 g−1 and individual flake thickness in the range of 1 to 5 nm. Nano powder was weighed with a Mettler AE-240 balance, whose accuracy is estimated to be 5 × 10–5 g, and then dispersed into a calculated volume of the base fluid, obtaining stable and homogeneous xGnP/FIL IoNFs, with percent weight concentrations of 1, 5, and 10 wt%. An ultrasonic bath  was used to obtain a correct dispersion. The FIL used in this case was 1-ethyl-3-methylpyridinium perfluorobutanesulfonate ([C2C1py][C4F9SO3]) (>99% mass fraction purity) supplied by Ionic Liquids Technologies. The thermophysical profile of this FIL has been determined in previous works [C4F9SO3] IoNF. Cooling was carried out using a refrigeration and thermostatic system able to reach 183.15 K. Dry nitrogen gas was used to purge the sample, with a 50 mL·min−1 flow rate. An amount of 12 to 15 mg of each studied sample was sealed in a standard aluminum sample tray, resistant to pressures up to 0.4 MPa. Eventual mass losses during the measuring process were avoided by weighing the sample holder before and after the experimental runs. This verification is essential for experimental data validation. IoNF samples were kept under sonication for one hour to ensure correct dispersion before each experimental measurement. DSC calibration was performed using indium  as standard. Samples temperature was then cooled to 183.15 K and maintained for 5 min. After that, they were heated to different temperatures, always respecting a minimum separation of 40 K between the last recorded transition and the cycle termination. Three repetitions of the cooling and heating cycles were performed for each sample at a constant 5 K·min−1 rate. This value of scanning rate ensures the best resolution and characterization of the various solid–fluid phase transitions. The DSC technique determines the thermal difference between two trays within a cell placed at the same conditions [C4F9SO3] were studied. A sample of 51 mg was heated from laboratory temperature to 1073 K at a constant rate of 5 K·min−1, maintaining an inert N2 atmosphere. Tonset = 686 K. Onset temperatures were obtained as the intersection between the baseline and the tangent to the derivative of the weight loss curve at the inflection point.Thermal stabilities of the FIL base fluid and the xGnP/[C2C1py][C4F9SO3], used as the base fluid, and of the three IoNF concentrations  were analyzed by temperature scans with cooling and heating rates of 5 K·min−1. In addition, repeated cycling tests were performed with five repetitions for each sample at the same conditions, certifying in all tests the same transitions.The solid–fluid transitions of the ionic liquid [C2C1py][C4F9SO3] and the xGnP IoNFs. The thermogram of the pure IL evidences that the material is polymorphic, presenting different solid phases in all cases. This behavior might be expected a priori because it has been shown [C4F9SO3] was also calculated due to its importance in different practical applications. This degree of supercooling can be computed from DSC results Strain SweepDuring the linear viscoelastic measurements, the linear viscoelastic regime (LVR) was determined as the first step by measuring both store (G′) and loss (G″) moduli of IoNFs in the strain range between 0.01% to 1000% at 10 rad·sIn these tests, the influence of the deformation of the fluids at a constant frequency on the viscosity was recorded, determining the region of linear viscoelasticity (LVR).−1 constant frequency, with a deformation range from 0.1% to 1000% and twenty points per decade.The values of storage (G′) and loss (G″) moduli, plotted against strain variation, are shown in As can be seen, the elastic component reached a point where torque was so low that the equipment found its measuring limit.It can be observed that the addition of nanoparticles produced an increase in both the elastic and viscous moduli because the dispersion had a higher resistance to flow. There was also a greater coherence in the data due to the arrangement in the structure of the ionic liquid with the aggregation of the nanoparticles.The linear regime in the viscoelastic behavior is clearly observed, where G″ was constant and strain independent. Afterward, for higher deformations, both moduli decreased for all samples. The structure resisted up to a certain tension when the internal structure was lost by the disaggregation of the suspended nanosheets.(2)Frequency SweepFor the pure FIL and 1 wt% IoNF, the viscous modulus was larger than the elastic one. However, at concentrations above the percolation concentration (described below), the elastic modulus G′ was higher than the viscous modulus G″. Then, the sample presented an interesting transition in its viscoelastic nature, evolving from a liquid-like a behavior at low concentrations, to a solid-like trend at higher graphene loads. Once the region of linear viscoelasticity was determined, the frequency tests were carried out with a selected deformation value of 0.1%.−1 at 293.15 K.(3)Graphene PercolationAs in strain sweep tests, the viscous component predominated over the elastic one in the entire region of linear viscoelasticity for the pure FIL, and it was the same case for low concentration of graphene. On the contrary, for concentrations above the percolation threshold, the opposite trend was observed. Then, the elastic modulus was predominant, being a feature of complex fluids approaching a solid-like trend. Both moduli increased with angular frequency, and also with the concentration of nanoparticles. This may be due to the structure of the pure liquid being unstable.The determination of the concentration of graphene percolation was obtained by extrapolation. Thus, five concentrations of graphene IoNF  were analyzed. Then, it was necessary to obtain the plateau of elastic modulus, from a frequency sweep within the (LVR), at the same conditions than frequency sweep, and 293.15 K, to ensure that all material was melted.0 is the threshold value for m, and the equation applies only in the vicinity of the percolation transition. This correlation is shown in The IoNFs experimental low frequency equilibrium moduli were modelled as a function of concentration following a percolation equation ,29:(2)loA value of the percolation concentration of 1.5% by weight was obtained, which demonstrates the solid internal structure of the IoNF due to the graphene addition.2C1py][C4F9SO3] at the same heating rates, with a 10 s−1 shear rate, obtaining the sample viscosity in these conditions, as displayed in Finally, temperature ramps were performed for the xGnP/[CSummarizing, samples of different concentrations of IoNFs obtained by dispersing graphene nanosheets in FIL have been analyzed in this work. The characterization of these IoNFs was carried out from the evaluation of the solid–fluid transitions using a differential scanning calorimetry technique, and in addition, a viscoelastic characterization was achieved using rheology. This allowed obtaining information about the internal structure of the IoNF, determining the influence of concentration of the suspended graphene.Previous research showed that 1-ethyl-3-ethylpyridinium perfluorobutanesulfonate is an optimum candidate to be used as a solvent in gas separation processes with a significantly reduced environmental impact. The formulation of IoNFs through the addition of graphene, or derivatives with a chemically functionalized surface, represents an option to enhance gas solubility, improving the performance in gas capture and separation.The obtained DSC profiles indicated a very complex thermal behavior of the solid–fluid transitions for the fluorinated ionic liquid, which are typical of polymorphic systems. For the pristine base fluid, a cooling ramp showed different exothermic peaks, revealing a succession of several polymorphic crystalline phases. This trend was remarkably modified on the addition of graphene nanosheets, resulting in an extremely rich and complex phase transition behavior. The final phase transition scenario combined features of enantiotropic and polymorphic solid–solid transitions for the pure IL, and the contributions of nucleation enhancement, solid–solid transition frustration and ultimate percolated structure due to the addition of the dispersed graphene nanosheets.The rheological profile found for these samples was also very complex. As a first and distinctive feature, all samples show non-Newtonian behavior, where modules G′ and G″ were practically constant in the low strain range, and both increased with graphene concentration, showing a clear transition from liquid-like to solid-like behavior with increasing nanoparticle load. A percolation concentration value of 1.5 wt% was obtained for this system, evidencing a solid internal structure of the IoNF.In conclusion, the addition of graphene allowed the phase transition and viscoelastic nature of the original FIL to be tuned. Graphene concentration controlled these two patterns, and small concentration variations produced a remarkable change in the system properties. Thus, the IoNF can be precisely tailored to match the required optimal features considering the use of this system in gas separation and capture applications.


New techniques to manipulate the electronic properties of few layer 2D materials, unveiling new physical phenomena as well as possibilities for new device applications have brought renewed interest to these systems. Therefore, the quest for reproducible methods for the large scale synthesis, as well as the manipulation, characterization and deeper understanding of these structures is a very active field of research. We here report the production of nitrogen doped bilayer graphene in a fast single step (2.5 minutes), at reduced temperatures (760 °C) using microwave plasma-enhanced chemical vapor deposition (MW-PECVD). Raman spectroscopy confirmed that nitrogen-doped bilayer structures were produced by this method. XPS analysis showed that we achieved control of the concentration of nitrogen dopants incorporated into the final samples. We have performed state of the art parameter-free simulations to investigate the cause of an unexpected splitting of the XPS signal as the concentration of nitrogen defects increased. We show that this splitting is due to the formation of interlayer bonds mediated by nitrogen defects on the layers of the material. The occurrence of these bonds may result in very specific electronic and mechanical properties of the bilayer structures. The absence of a band-gap, however, has prevented its implementation as a possible replacement in semiconductor industry10.Since graphene was effectively isolated in 200414 studies have demonstrated that an effective option towards its electronic modulation is nitrogen doping, which generates n-type extrinsic electrical conductivity15. The substitution of carbon by nitrogen disrupts the ideal sp2 hybridization of graphene’s lattice, locally inducing significant changes to its electronic properties, separating the conduction band from the valence band even at modest concentrations of 2%16. This possibility of tunning graphene’s electronic properities could lead to potential applications in electronic devices, from electrochemical biosensors to spintronics18.For monolayer graphene, both theoretical and experimental10. Last year, Y. Cao et. al. have shown that by twisting the relative angle between two graphene sheets the resulting electronic properties of the system can vary from a Mott-insulator19 to a superconductor20. Other groups have since then shown that twisting the angle between the layers of other 2D heterostructures - in what has been called twistronics - has also been achieved for materials such as MoSe2-WS221 and even heterostructures composed of three layers of atomic sheets alternating 2D Boron Nitride sheets with graphene monolayer22. In all of these cases, the appearance of a new periodicity in the rotated crystalline systems - called a moiré superlattice - results in new electronic properties of the systems. These properties can be so drastically different and so sensitive to the twist angle that changes in the relative rotation of the layers by no more than 0.2 degrees can cause all the remarkable effects observed to disappear20. The effective combination of 2D materials into heterostructures already appeared as a technique that would unveil an enormous new set physical properties that could be employed for a vast set of possible technological applications. The realization that the properties of these systems can be so dramatically altered by twisting the angle of stacking these monolayers indicates that these new possibilities stretch even further23.Another possibility for implementing a band-gap in graphene is working with few layer graphene films. It has long been known that the application of an electric field can induce the appearance of a band-gap in bilayer graphene25. Another relevant feature observed was the shifting of 2D peaks to lower energies, related to the introduction of electron donors (such as nitrogen) into the samples32. Using XPS we confirmed the presence of nitrogen defects in the synthesized material, identifying that pyridine defects prevail in our samples. However, as the concentration of nitrogen in the bilayer graphene is increased, we observed in the high resolution N1s spectrum an unexpected splitting in the peak associated to these defects.These exciting new findings have brought renewed interest to the study of bilayer grahene and methods to synthesize, dope and characterize these systems. Moreover, the comprehension of how atomic defects specific to bilayer systems affect their electronic and mechanical properties is of paramount importance. In this work we demonstrate a plasma-enhanced chemical vapor deposition (PECVD) process for the synthesis and doping of graphene bilayer structures in a single step. By carefully adjusting the variables of the plasma , and flow ratio of gases  we were able to obtain nitrogen-doped bilayer sheets in one single fast process (2.5 minutes). Moreover, we were able to carry out this process at low temperature (760 °C). Through Raman spectroscopy we could verify the effectiveness of the synthesis, determining that we obtained large area graphene bilayer films. We also confirmed doping thought the appearance of D′ band, attributed to the defect-induced intravalley double resonance scattering process33. In this conformation, an interlayer covalent bond forms, linking defects on the two layers of the material. We used quantum-mechanical parameter-free simulations, based on the density functional theory, to test this hypothesis. We simulated several defect complexes involving nitrogen and carbon vacancies, including pyridine and pyrrolic in different orientations. We also investigated how these defects could interact with a second layer of graphene, both pristine or containing other defects, employing different levels of simulations, with and without the explicit inclusion of van der Waals interaction. The ideal relative proportions of these defects in the synthesized material is readily given by their formation energies. For some defect complexes, we observed that the formation of covalent bonds between nitrogen in one layer with one of the atoms in the layer adjacent to it is stable. We then performed calculations of level shifts associated to core electrons of nitrogen in different sets of defects, simulating the XPS signature of each of these defects complexes, comparing to our experimental data. We conclude that the splitting of the XPS peak in our bilayer graphene samples as the concentration of nitrogen increased is likely related to the formation of the interlayer bonds mediated by nitrogen defect complexes. The presence of these interlayer covalent bonds can affect the mechanical as well the electronic properties of bilayer graphene in significant contrast to the way these defects modulate the properties of monolayer graphene. Therefore, the induction or suppression of such defects can represent another parameter to be taken into consideration when tailoring graphene and other bilayer or few layer heterostructures for specific applications.If the observed splitting in the XPS peak was associated to new types of defects becoming more frequent in the material as the concentration of nitrogen increases, we would expect similar behavior to occur in the monolayer graphene, but we have found no reports of a similar splitting in the literature for this material. Therefore, we hypothesized that this splitting could be caused by new conformations of the usual pyridinic defects exclusive to bilayer graphene, called clipping2/Si substrate for pristine (PG) and nitrogen-doped samples . The intensity, the shape and the peak position are strong indicatives of the number of layers, graphene structure and type of dopants36.We performed Raman analyses of our graphene samples to study their structural properties as well as dopant content. Figure D2/IG) and by the full width at half-maximum (FWHM) of the 2D band37. A monolayer graphene usually presents ID2/IG values of 2 or bigger with a FWHM ~30 cm−1, while bilayer structures present this intensity ratio ranging from around 1 to 1.5 and a FWHM ~50 cm−1 40. Therefore, the structures synthesized in our experiments show typical intensity relation values of bilayer graphene. Raman mapping studies also confirm the high-yield growth of bilayer graphene as can be seen in Supplementary Information Fig. 2 substrate, as well as careful removal of carbon and other impurities left on the sample during the transfer to the substrate. Therefore, we believe that the Raman analyses described above is more indicated in our case.In the spectra of all samples, Fig. −1 (D band), and an additional peak located at ~1620 cm−1 (D′ peak) are also observed. The D and D′ peaks are activated by inter and intra-valley double resonant Raman process25, in which the defects are responsible for the missing momentum that completes the resonant process. Therefore, the increase in the concentration of defects leads to an increase in both intensities42. Pristine sample shows a moderate amount of defects (D band) even without nitrogen doping. This is expected as the growth occurs in the presence of hydrogen plasma, which would contain energetic ions and radicals that induce defect formation44.Peaks at 1350 cm45. With the increase of nitrogen incorporated in the sample, the D band increase together with the 2D band decrease and broadening then indicates the formation of more disordered structures32, which is further confirmed by the increase on the ID/IG ratio39. Moreover, it has been shown that the in-plane vibrational G band of graphene and bilayer graphene is blue-shifted for both electron and hole doping, due to the non-adiabatic removal of the Kohn anomaly at the Γ point. On the other hand, the 2D band is red-shifted for electron doping and blue-shifted for hole doping, due to the charge transfer induced modification of the equilibrium lattice parameter46.The 2D peak, on the other hand, originates from two-phonon double resonant process, which does not need defects to satisfy the resonant condition. Its intensity is affected by the electron/hole scattering rate. The presence of nitrogen dopants in graphene would both create defects and introduce electron doping, which increases the electron scattering rate and, consequently, decreases the intensity of the 2D peak−1, 2684 cm−1 and 2679 cm−1, while the blue-shifted G peaks in 1593 cm−1, 1594 cm−1 and 1595 cm−1 for NG1, NG2 and NG3 samples, respectively. Therefore, the value and evolution on peak intensities combined with position shift consolidate strong evidences of bilayer graphene formation, with nitrogen dopants concentration increase proportional to nitrogen flow during the PECVD process.In our analysis, we found that the 2D band red-shifts while the G band blue-shifts as the nitrogen content increase during the synthesis process. In Fig. 2 hybridized carbon and confirms the formation of graphene during the synthesis. A second and third bands, centered at 286 eV and 288.5 eV obtained by deconvolution of previously described peak, reflects the binding of both oxygen or nitrogen to sp2 C and sp3 C respectively36. Previous studies47 show that N-doped graphene is more susceptible for oxygen functionalization. This fact might be ascribed to the increased reactivity of atoms at the edge of defects and pyridinic N sites. These sites react with oxygen-containing species when exposed to the atmosphere, creating a sample with higher oxygen content. As our samples were exposed to laboratory air before the XPS analysis, the oxygen signature might be a consequence of the described phenomenon. The higher intensity at α-C sp3 position and of α-C sp2 band with the increase in nitrogen corroborates the observed Raman results, as these changes are caused by the increase of ligands concentration, which further confirms graphene doping.XPS was used to investigate chemical state changes on graphene structure as a result from nitrogen flow increasing during the synthesis, i.e., doping process. Fig. 48. Figure Nevertheless, a proper comprehension of chemical state changes required the analysis of high resolution N spectra, which are presented in Fig. 3N, in which the compound was fabricated by reactive radio frequency sputtering of copper targets in nitrogen and argon environment49. Nevertheless, it is known that copper nitrate is not stable at temperatures higher than 400 °C49. Therefore, the formation of such compound is inconsistent with our experimental conditions (760 °C temperature synthesis), which indicates that the observed peak might arise from other binding structures present in doped bilayer graphene. To further investigate the chemical state changes detected by our XPS measurements, we performed fully quantum atomistic simulations based on the density functional theory (DFT) method.Previous studies have assigned this second peak in the XPS analysis to N–Cu bonding in CuIn Fig. 54 which aims to accurately describe van der Waals forces in DFT simulations. The interlayer distances of AB and AA stackings considering vdW forces remained nearly the same as those obtained with LDA 3.34 Å and 3.54 Å. The binding changed markedly to −33 meV per atom, or three times more than predicted by LDA. We also simulated defects using optB86b + vdW-DF functional. In the Supplemental Information Fig. We expect that coulomb interaction will favor the formation of complexes composed by defects of opposing charges, specially in bilayer graphene, in which defects of opposite charge on different layers can approach each other more easily than in the monolayer. In the Supplemental Information we discuss how we estimate the electronic state of the defects indicated in Fig. 33, and is illustrated in Fig. Ef) calculated in the same supercell, and the difference of this formation energy relative to the sum of the formation energies of the defective monolayers that make up the defect complex (ΔEf). This difference in energy gives an idea of the interaction energy between the defective monolayers when combined. We also indicate whether the interlayer covalent bond remained stable during the optimization of the atomic positions in our simulations of these complexes and what are the formation energies when interlayer bonds do remain stable. As discussed previously for monolayer graphene and the bilayer with one isolated defect, we tested the effect of optB86b + vdW-DF functional in these results, as shown in Tables Having established that isolated defects in monolayer and bilayer graphene are not markedly different and thus are not expected to produce different XPS signatures, we investigate complexes of defects in the bilayer. Specifically, complexes in which defects are in adjacent layers. The proximity of two defects will not only induce structural distortions through electrostatic interaction, but also due to the presence of frustrated bonds, that can lead to significant reconstructions. In some cases, the interaction of defects in opposite layers of the bilayer can lead to the formation of covalent bonds between the layers. This phenomenon is called clippingi.e. different bonding arrangements).As shown in Table 12.We simulated the XPS experiment in our defective graphene bilayers computing the electron core level shift for different configurations of the defects. The core level shifts are not only related to the orbital of the electron within the nucleus of an atom but also to how this atom bonds with its neighbors. The difference in the bonding structure of an atom results in different binding energies for core electrons in the same atom. This energy difference is usually enough to distinguish between a nitrogen in the substitutional defect, Fig. 55. In this method, we compute the energy difference of a system in its ground state with the same system with one electron removed from the core of a specific atom. In the final state approximation, the electrons are allowed to relax, screening the localized core hole. This energy difference gives the binding energy of the respective core electron. Absolute energy values are not meaningful, but the core level shift, that is, the difference of the binding energy of the same core electron in different chemical environments  is accurate to within 50 meV55. Our referential is the core eigenvalue of one 1 s electron from substitutional nitrogen on monolayer graphene, see Fig. ECLS is the energy of the core level shift, c in the configuration of interest and s electron from substitutional nitrogen on monolayer graphene, this energy is −406.4 eV.We computed the core eigenvalues using the final state approximation within VASPWhen considering the combination of a monolayer containing a nitrogen defect and a pristine monolayer, we observed an almost rigid translation in the core level shift, as shown in Table When defects in adjacent layers interact, a significant difference between clipped and not-clipped structures can be seen Table . ConsideWhen a pyrrole defect occurs in the geometry shown in Fig. As stated in the Experimental Results and Discussion, the analyses of the XPS survey . To further verify this conclusion, we analyzed cooper defects on monolayer and bilayer graphene. In Fig. Finally, we also investigated defect complexes involving copper impurities. Since we used Cu substrate to grow and analyze our samples, there is a possibility that this impurity could be responsible for some of the features observed in our experimental XPS analyses. As a matter of fact, other authors have proposed this interaction of Cu with N defects as the cause of the splitting in the XPS peak that we have also observed56.The results of formation energies and CLS regarding the copper-related defect complexes described above can be found in Table Considering the CLS of nitrogen atoms in the copper-related defect complexes, from monolayer to bilayer graphene, we verified a rigid shift of at most 2 eV among the results of all the analyzed defects. Moreover, the nitrogen CLS energies of the copper-related defect complexes are about 20 eV distant from those values obtained for the nitrogen defects  and high crystallinity. The plasma/metal coupling causes localized, rapid heating of the foil, leading to a fast synthesis (2.5 min). This localization also reduces the post-growth cooling time (10 min). The combination of hydrogen, nitrogen and methane plasma serves to remove the copper native oxide layer as the synthesis occur, minimizing the number of steps taken to obtain a stable graphene sheet. It was also shown that the change in N2 during synthesis. These nitrogen related defects must be further studied, but are expected to alter mechanical and electronic properties of bilayer graphene in a markedly different way from monolayer graphene. These defects are likely to be present in other types of few layer 2D films and heterostructures and we indicate a route to identify their occurrence through XPS characterization and computer simulations. Moreover, their are likely to play a role in the new field of twistronics that needs to be further investigated. The suppression or incorporation of these defects in 2D few layer structures can be another parameter to tune in order to adjust their properties for specific applications.We performed state-of-the-art, parameter-free simulations based on the density functional theory (DFT) to verify the cause of this splitting of the XPS signal. Using the local density approximation as well as implementations of the DFT that explicitly include van der waals interactions (optB86b + vdW-DF) we obtained the same trend for the formation energy of several monolayer graphene defects, bilayer graphene defects and defect complexes. When defects are close enough in adjacent layers, they can induce the formation of interlayer covalent bonds, a phenomenon known as “clipping”. Calculations of the shifts in core level energy of these interacting defect complexes suggest that the features observed in the high resolution N1s XPS spectrum of doped samples (NG2 and NG3) are related to the clipping of defect complexes in bilayer graphene. The formation of interlayer bonds changes the chemical environment around the impurity atom, affecting the core electron binding energy detected by the XPS measurement. Our simulations show that interaction between defect complexes are highly dependent on experimental conditions and can be tuned by controlling the ratio of N57. In this system, the sample is mounted onto the top of a quartz tube cap inside the plasma chamber, and the bottom surface of the sample can be viewed by an optical pyrometer positioned outside the chamber. Copper films with dimensions of 5 × 5 × 0.025 mm3 were used as substrates. First the MW-PECVD chamber was pumped down to 15 Torr, followed by an argon purge, in which the system reached a pressure of 30 Torr. The synthesis was then started by plasma activation. To start the plasma, the microwave power was adjusted to a value of 500 W, resulting in a voltage of 85 V, and generating a locally confined plasma of ~5 cm diameter around the sample. The plasma parameters resulted in the heating of the substrate to 760 °C, which remained constant throughout the synthesis. After the plasma activation, the argon flow was interrupted and the 10 standard-cubic centimeters-per-minute (s.c.c.m.) of room temperature hydrogen gas was added. Keeping the hydrogen flow constant, several flow ratios of methane and nitrogen gases were added into the chamber, as described in Table Graphene films were grown in an in-house built microwave plasma-enhanced chemical vapor deposition (MW-PECVD) system, which configuration is described in details elsewhere2 layer on Si by PMMA transfer method in order to obtain a spectra without copper background reflectance interference58. Raman spectroscopy was performed on a confocal micro-Raman spectrometer (Witec Alpha 300R), equipped with a 532 nm laser source (power at sample below 0.1), 100× objective and charge coupled device (CCD) detector. The system generated a laser spot size of 300 mm (diameter) and spectral resolution of 3 cm−1. The wavenumber calibration was based on standard values for crystalline silicon band checked at 532 cm−1. Measurements were carried out at room temperature and peaks were fitted using a Lorentzian function.For Raman spectroscopy, graphene films were transferred onto a 300 nm thick SiOKα radiation (hν = 1486.6 eV) and pass energy of 10 eV, operating at base pressure of 3e-9 Torr. Background was subtracted according to Shirley model, and peak fitting was performed with a product of Gaussian and Lorentzian shapes. Initial quantification was based on Scofield sensitivity factors59 and a model of uniform elemental distribution. The chemical composition of the samples  were estimated from survey XPS spectra as shown in Fig. Film composition was investigated by X-ray photoelectron spectroscopy (XPS) using a UNI-SPECS UHV spectrometer with monochromatic Al ab initio total energy calculations based on the density functional theory (DFT)61 with the Vienna Ab Initio Simulation Package (VASP)63 using the local density approximation (LDA)64. Results obtained by this approximation were compared with results using van der Waals corrections through optB86b + vdW-DF exchange and correlation functional54. For the Brillouin zone sampling, an optimized Γ-centered 1 × 1 × 1 mesh was used for geometry relaxations and Γ-centered 2 × 2 × 1 for electronic properties. The atomic positions in all structures were relaxed using the CG algorithm until residual forces were below 0.025 eVÅ−1. Pristine monolayer and bilayer graphene supercells considered have 160 and 320 atoms, respectively. We used 20 Å of vacuum in the z direction to prevent spurious interaction between our simulated supercells and their images given that periodic boundary conditions (pbc) are used. The projector augmented-wave method (PAW)65 was used and the kinetic energy cutoff for the plane wave expansion was optimized to 560 eV.All simulations were performed with Characterization of nitrogen doped graphene bilayers synthesized by fast, low temperature microwave plasma-enhanced chemical vapor deposition


The root system architecture (RSA) of plants is highly dependent on the surrounding nutrient environment. The uptake of essential nutrients triggers various signaling cascades and fluctuations in plant hormones to elicit physical changes in RSA. These pathways may involve signaling components known as small signaling peptides (SSPs), which have been implicated in a variety of plant developmental processes. This review discusses known nutrient-responsive SSPs with a focus on several subclasses that have been shown to play roles in root development. Most functionally well-characterized cases of SSP-mediated changes in RSA are found in responses to nitrogen (N) and phosphorus (P) availability, but other nutrients have also been known to affect the expression of SSP-encoding genes. These nutrient-responsive SSPs may interact downstream with leucine-rich repeat receptor kinases (LRR-RKs) to modulate hormone signaling and cellular processes impacting plant root development. SSPs responsive to multiple nutrient cues potentially act as mediators of crosstalk between the signaling pathways. Study of SSP pathways is complicated because of functional redundancy within peptide and receptor families and due to their functionality partly associated with post-translational modifications; however, as genomic research and techniques progress, novel SSP-encoding genes have been identified in many plant species. Understanding and characterizing the roles of SSPs influencing the root phenotypes will help elucidate the processes that plants use to optimize nutrient acquisition in the environment. Plants require macronutrients and micronutrients from the soil to grow and develop. Due to various geological processes and soil chemistry, these nutrients are heterogeneously distributed in patches and gradients . Thus, pOne way in which plants sense changes in nutrient availability in the environment and convey the information to downstream physiological processes is through the expression of nutrient-responsive small signaling peptides (SSPs). SSPs are a class of proteins ranging from 5–75 amino acids in length. Many SSPs are derived from nonfunctional precursor proteins, which are cleaved and post-translationally-modified to mature forms which may then carry out their function as short- or long-distance mobile signals. However, other SSP families may be derived from functional precursors, the 5’ region of mRNA, primary transcripts of miRNA, or small open reading frames (sORFs) ,7. TheseRegardless of the current lack of receptor identification, SSPs have been implicated in a broad range of downstream processes throughout the plant, such as meristem maintenance, cell proliferation and expansion, reproduction, and response to pathogens ,11,12,13This review examines how nutrient signals are interpreted by SSP pathways to alter root morphology for the improvement of nutrient uptake. Most current evidence suggests that a few SSP families play important roles in regulating macronutrient-responsive changes in RSA, particularly in regard to N- or P-availability or maintaining nutrient uptake and homeostasis . However3−) is highly mobile and easily leaches due to the flow of water; in certain plant species, this may encourage the root system that is “steep, cheap, and deep” to acquire NO3− that has stratified in the lower soil levels [4+) can elicit different changes in RSA. When N-depleted plants grow into a NO3−-rich patch, lateral root elongation is stimulated in order to acquire more N, but in NH4+-rich patches, more lateral roots are initiated and existing roots become more branched [3− conditions, the RSA profile exhibits long primary roots with short lateral roots, while excess NH4+ inhibits primary root growth [3− supply after a period of N-starvation also induces root hair development [3− modulates RSA has been studied in depth and involves complex interactions between the NITRATE TRANSPORTER 1.1 (NRT1.1) and auxin signaling pathways [Variation in availability of the essential macronutrient nitrogen (N) is known to elicit significant changes in plant RSA. Within the soil profile, nitrate  gene expression is induced, and mature CLE3 peptides are produced and secreted from the root pericycle, which bind to the CLV1 LRR-RLK expressed in the phloem companion cells. This interaction signals to yet uncharacterized downstream components to repress the lateral root development. This model was suggested from an experiment validating the ligand-receptor relationship in transgenic Arabidopsis lines that overexpress CLE3, which have a decreased lateral root density correlating with the increased CLE3 transcript accumulation when CLV1 is present [CLE3 peptide-encoding gene is induced by NO3−-starvation, it is induced by NH4+ supply after N-starvation [N-availability alters the expression of various SSPs linked to these observed changes in RSA. One peptide family in which select members have exhibited N-responsiveness is the CLAVATA3 EMBRYO SURROUNDING REGION RELATED (CLE) family. CLEs are 12–13 amino acid peptides that control plant development at various stages. While there are thirty-two CLE family members in Arabidopsis, more have been identified in leguminous species where CLE peptides are known to be involved in the autoregulation of nodulation (AON) in addition to RSA response ,23. The  present . Additioarvation , indicatCEP genes leads to the repression of primary root growth and lateral root initiation, while the knockout lines of CEP3 generate larger root systems with a higher lateral root density under N-limited conditions [CEP gene expression increases in roots undergoing local N-starvation, and mature CEP peptides translocate to the shoot as long-distance signals to interact with the CEPR1 and CEPR2 LRR-RKs, which induces a shoot-derived signal mediated by the CEP DOWNSTREAM1 (CEPD1) and CEPD2 polypeptides [Medicago truncatula, low nitrogen upregulates MtCEP1, which interacts with the CEPR1 homolog COMPACT ROOT ARCHITECTURE 2 (MtCRA2) to inhibit the lateral root growth [Another SSP family with members exhibiting responsiveness to N is the C-TERMINALLY ENCODED PEPTIDE (CEP) family. The fifteen members of this family of Arabidopsis are processed to mature 15-amino-acid peptides hydroxylated at the proline residues . Overexpnditions . The CEPpeptides . This sipeptides . Despitepeptides . In Medit growth ,33. Addit growth .Phosphorus (P) is another essential macronutrient known to impact RSA. P is relatively immobile in the soil and is typically localized in higher concentrations in the topsoil layer . P-deficRGF1, RGF2, and RGF3 are induced in the meristematic cortex and epidermis of the root tip under inorganic phosphate (Pi) deprivation [RGF2 exhibit short primary roots and a hypersensitivity to low P-environments, while RGF1 was shown to control circumferential root growth through the repression of radial cellular divisions in response to a Pi deficiency. These changes are proposed to occur through the RGF-mediated signaling pathways, acting on manipulating gradients of PLETHORA (PLT) transcription factors along the root [One of the best characterized SSP families responsive to P-availability is the ROOT GROWTH FACTOR/GOLVEN/CLE-LIKE (RGF/GLV/CLEL) family. rivation . These trivation . A more the root . Throughthe root . RGF-pepthe root ,37.M. truncatula. Members of the INFLORESCENCE DEFICIENT IN ABCISSION (MtIDA), PLANT PEPTIDE CONTAINING SULFATED TYROSINE (MtPSY), and PAMP-INDUCED SECRETED PEPTIDE (MtPIP) families are upregulated upon P-deficiency, but exogenous application of synthetic MtIDA18, MtPSY2, and 9 MtPIP peptides enhances the total root length, especially of the primary root [M. truncatula.While the RGF/GLV/CLEL family regulates RSA in response to P-availability in Arabidopsis, other families have been suggested to play a role in the response based on experiments in ary root . It is, Nicotiana attenuata, NaRALF1 additionally promotes root hair formation, which is proposed to improve P-uptake [SSP pathways governing root developmental processes may still influence the plant’s ability to acquire various nutrients, even if the expression of the SSP-encoding gene is not responsive itself to nutrient availability. The function of the CASPARIAN STRIP INTEGRITY FACTOR (CIF) sulfated peptides represents an example of such constitutive actions of SSPs ,45. CIF1P-uptake ,48. ThesOther non-nutrient responsive SSP pathways are known to impact and modulate RSA, which may indirectly affect nutrient uptake. Many of these established pathways have been shown to have downstream effects on cell structure and maintenance. Among the members of the CLE family, CLE40-ACR signaling is integral for the maintenance of the root apical meristem . IDA pep3− supply, but this response is ablated in the tga1/tga4 double knockout; however, the direct effect of CAPE expression on root hair phenotype, once again, remains to be observed or functionally characterized.Only a small percentage of SSPs have been functionally characterized for their involvement in nutrient response and RSA. Potential candidates for further research are largely identified after their nutrient responsiveness are exhibited at the transcriptional level. Relevance of SSPs to regulatory pathways responding to nutritional availability and their potential influence on root development can be mined from the transcriptome datasets examining the effect of nutrient supply on gene expression or RSA. Microarray data of Arabidopsis grown on split-root media, with half of the root system exposed to N-replete environments and the other half in N-deficient conditions, shows a differential expression of thirty-one SSPs (including various CLEs and CEPs) from multiple SSP families ,51. ThesM. truncatula transcriptome after S- and K-deprivation showed that seventy-two SSP-encoding genes were differentially regulated by S, and one (MtLegin13) was downregulated by a K-deficiency [N and P are indeed the well-characterized macronutrients affecting the SSP-mediated changes in RSA, likely because they induce the most dramatic visible changes in root morphology. However, other macronutrients, such as sulfur (S) and K, as well as various micronutrients may also act through SSP pathways to influence growth and metabolism in the root. Analysis of ficiency . AdditioOne further challenge to interpret the impact of nutrients on the RSA is that most of the experiments reported in the literature only examine the effect of a single nutrient. Plants growing in soil, however, experience combinatorial nutrient cues that could independently cause contradictory changes in RSA. The moderate effects of certain nutrients, such as S and K, on RSA may also be magnified when combined with fluctuations in other nutrient availabilities . CrosstaCLE3 expression is repressed by nitrate, the more desirable source of N used by the plant, but highly induced by ammonium [3− but also to sulfate and phosphate, while other CLE family members exhibit responsiveness to glucose and iron availabilities [LjCLEs) in the Lotus japonicus have been shown to be upregulated in response to P-supply, although these have not been linked to any RSA changes [M. truncatula to N, P, K, and S starvation and resupply. Two hundred forty nutrient-responsive SSPs were purportedly involved in the receptor-mediated signaling; while 61% of these genes were responsive to a single macronutrient deprivation in the roots, the remainder, including the MtCLE family members MtCLE05 and MtCLE34, were responsive to two or three conditions [These cues may be derived from different functional forms of the same nutrient. For example, ammonium ,55. Thisbilities . Corrobo changes . These tnditions . ResearcM. truncatula, recent genome re-annotations have identified 1970 homologs of known SSP gene families and 2455 potentially novel SSP-encoding genes [http://bioinformatics.psb.ugent.be/webtools/PlantSSP/, accessed on 8 November 2018), which predicts close to 40,000 SSPs and over 4000 SSP families based on the annotations of 32 plant species, many of which have agricultural relevance [The field of SSP research is greatly expanding. In Arabidopsis, over a thousand peptide encoding genes have been identified . Likewisng genes . Increaselevance . DespiteMany experiments performed to study SSPs have relied on forward and reverse genetics using transgenic overexpression lines of peptide-encoding genes or knockout mutant lines. One study has also suggested the use of antagonistic peptides to artificially generate a dominant-negative effect to examine the loss of peptide activity . HoweverAnother specific consideration is that many experiments rely on the use of exogenously applied synthetic peptides. While these may indeed phenocopy the transgenic overexpression lines and provide a relatively quicker means of analysis, many synthetically-generated peptides lack post-translational modifications that are necessary for proper biological functioning of the SSPs . Conservclv1 bam double mutant results in a much more severe phenotype than the single mutants in clv1 or bam [Lastly, challenges are present not only in studying SSPs themselves, but also in determining associated LRR-RKs. Many studies on the downstream developmental effects of SSP pathways rely on using the mutant lines of proposed receptors. However, SSPs may bind to multiple receptors and individual receptors may also interact with multiple SSPs ,44,45. F1 or bam . This re1 or bam ,68,69,701 or bam . Aside f1 or bam ,72, or w1 or bam ,74.M. truncatula may be further translated in crops or other leguminous species. More directly, application of validated synthetic peptides to seeds or plants growing in nutrient-limited environments is proposed as a non-transgenic means to improve plant growth and yield. Current research in the field has identified the key roles of SSPs in N- and P-responsive root development, as well as other SSPs either responsive to other nutrients or influential in nutrient uptake and homeostasis. Large-scale genomic datasets have also introduced a better understanding of the number and variation in the putative SSP families, which could potentially impact the RSA or be involved in the crosstalk of various nutrient cues. Further research advancement in this area to elucidate the roles of potentially hundreds of nutrient-responsive SSPs is imperative to fully understand the integration of nutrient cues in RSA phenotypes.While there remain significant technical challenges, SSP signaling modules identified recently have shown a great promise as regulators of root development, controlled in response to nutrient environmental cues. SSP signaling and biology can be applied to various agricultural practices. Improving the basic understanding of these pathways and how they mediate nutrient-responsive changes in the RSA in model organisms such as Arabidopsis and