High Moisture-Barrier Performance of Double-Layer Graphene Enabled by Conformal and Clean Transfer.

Graphene films that can theoretically block almost all molecules have emerged as promising candidate materials for moisture barrier films in the applications of organic photonic devices and gas storage. However, the current barrier performance of graphene films does not reach the ideal value. Here, we reveal that the interlayer distance of the large-area stacked multilayer graphene is the key factor that suppresses water permeation. We show that by minimizing the gap between the two monolayers, the water vapor transmission rate of double-layer graphene can be as low as 5 × 10-3 g/(m2 d) over an A4-sized region. The high barrier performance was achieved by the absence of interfacial contamination and conformal contact between graphene layers during layer-by-layer transfer. Our work reveals the moisture permeation mechanism through graphene layers, and with this approach, we can tailor the interlayer coupling of manually stacked two-dimensional materials for new physics and applications.

CsPbI3-Based Phase-Stable 2D Ruddlesden-Popper Perovskites for Efficient Solar Cells.

Inorganic CsPbI3 perovskite has shown great promise in highly stable perovskite solar cells due to the lack of volatile organic components. However, the inferior phase stability in ambient conditions resulted from the very small Cs+, limiting their practical applications. Here, CsPbI3-based 2D Ruddlesden-Popper (RP) perovskites were developed using two thiophene-based aromatic spacers, namely, 2-thiophenemethylamine hydroiodide (ThMA) and 2-thiopheneformamidine hydroiodide (ThFA), which significantly improved the phase stability by releasing the large inner stress of black-phase CsPbI3. The optimized ThFA-based 2D RP perovskite (n = 5, ThFA-Cs) device achieves a record efficiency of 16.00%. Importantly, the ThFA-Cs devices could maintain an average of 98% of their initial efficiencies after being stored in N2 at room temperature for 3000 h and 92% of their initial value at 80 °C for 960 h. This work provides a new perspective for exploration of the phase-stable CsPbI3-based perovskite with reduced dimensions for high-performance solar cells.

Multiple-Noncovalent-Interaction-Stabilized Layered Dion-Jacobson Perovskite for Efficient Solar Cells.

Two-dimensional Dion-Jacobson (DJ) perovskites have shown improved structure stability in comparison with Ruddlesden-Popper (RP) perovskites. However, the mechanism behind the improved stability is still largely unexplored. Here a multifluorinated aromatic spacer, namely, 4F-PhDMA, has been successfully developed for 2D DJ perovskites. It is found that the 2D DJ perovskite with a 4F-PhDMA spacer exhibits a high dissociation energy due to the multiple noncovalent interactions. The optimized 2D DJ device based on the 4F-PhDMA spacer (n = 4) exhibits a champion efficiency of 16.62% with much improved light and thermal stability. This efficiency is much higher than that of the control device using an unfluorinated spacer (n = 4, PCE = 10.11%) and is among the highest efficiencies in aromatic-spacer-based 2D DJ perovskite solar cells (PSCs). Our work highlights the importance of incorporating multiple noncovalent interactions in the 2D DJ perovskite by employing a multifluorinated aromatic spacer to achieve DJ PSCs with both high efficiency and high stability.

Overcoming the Anisotropic Growth Limitations of Free-Standing Single-Crystal Halide Perovskite Films.

It is extremely challenging to grow single-crystal halide perovskite films (SCHPFs) with not only desired transport properties but also large lateral size with much thinner thickness. Here, we report the growth of freestanding single crystal CsPbBr3 SCHPFs with thickness less than 100 nm and a lateral size close to centimeter for the first time. A new model for growth kinetics (Ψ=Aexp[-(EA -Es )/(kB T)]) is proposed to address the surface energy and temperature effect on the growth rate of ultrathin CsPbBr3 single-crystal film. The experimental results and DFT calculations both demonstrated that the surfactant plays a critical role in modifying the surface energy and achieving anisotropic growth. This work opens new opportunities for high-quality SCHPFs with large lateral size and controllable thickness that may find wide applications for optoelectronic devices.

Superclean Growth of Graphene Using a Cold-Wall Chemical Vapor Deposition Approach.

Chemical vapor deposition (CVD) has become a promising approach for the industrial production of graphene films with appealing controllability and uniformity. However, in the conventional hot-wall CVD system, CVD-derived graphene films suffer from surface contamination originating from the gas-phase reaction during the high-temperature growth. Shown here is that the cold-wall CVD system is capable of suppressing the gas-phase reaction, and achieves the superclean growth of graphene films in a controllable manner. The as-received superclean graphene film, exhibiting improved optical and electrical properties, was proven to be an ideal candidate material used as transparent electrodes and substrate for epitaxial growth. This study provides a new promising choice for industrial production of high-quality graphene films, and the finding about the engineering of the gas-phase reaction, which is usually overlooked, will be instructive for future research on CVD growth of graphene.

In Situ Electron Driven Carbon Nanopillar-Fullerene Transformation through Cr Atom Mediation.

The promise of sp2 nanomaterials remains immense, and ways to strategically combine and manipulate these nanostructures will further enhance their potential as well as advance nanotechnology as a whole. The scale of these structures requires precision at the atomic scale. In this sense electron microscopes are attractive as they offer both atomic imaging and a means to structurally modify structures. Here we show how Cr atoms can be used as physical linkers to connect carbon nanotubes and fullerenes to graphene. Crucially, while under electron irradiation, the Cr atoms can drive transformations such as catalytic healing of a hole in graphene with simultaneous transformation of a single wall carbon nanotube into a fullerene. The atomic resolution of the electron microscopy along with density functional theory based total energy calculations provide insight into the dynamic transformations of Cr atom linkers. The work augments the potential of transmission electron microscopes as nanolaboratories.

Thermodynamic Stability Trend of Cubic Perovskites.

Stability is of central importance in current perovskite solar cell research and applications. Goldschmidt tolerance factor (t) recently provided qualitative guidance for experimentalists to engineer stable ABX3 perovskite by tuning effective ionic size with mixing cations or anions and for theorists to search emerging perovskites. Through first-principles calculations, we have calculated decomposition energies of 138 perovskite compounds of potential solar cell applications. Instead of t, we have found that (µ + t)η, where µ and η are the octahedral factor and the atomic packing fraction, respectively, demonstrates a remarkably linear correlation with thermodynamic stability. As a stability descriptor, (µ + t)η is able to predict the relative stability among any two perovskites with an accuracy of ∼90%. This trend is then used to predict decomposition energies of another 69 perovskites, and the results are in excellent agreement with first-principles calculations, indicating the generalization of the trend. This thermodynamic stability trend may help the efficient high-throughput search for emerging stable perovskites and precise control of chemical compositions for stabilizing current perovskites.

Two-Dimensional SiS Layers with Promising Electronic and Optoelectronic Properties: Theoretical Prediction.

Two-dimensional (2D) semiconductors can be very useful for novel electronic and optoelectronic applications because of their good material properties. However, all current 2D materials have shortcomings that limit their performance. As a result, new 2D materials are highly desirable. Using atomic transmutation and differential evolution global optimization methods, we identified two group IV-VI 2D materials, Pma2-SiS and silicene sulfide. Pma2-SiS is found to be both chemically, energetically, and thermally stable. Most importantly, Pma2-SiS has shown good electronic and optoelectronic properties, including direct bandgaps suitable for solar cells, good mobility for nanoelectronics, good flexibility of property tuning by layer control and applied strain, and good air stability as well. Therefore, Pma2-SiS is expected to be a promising 2D material in the field of 2D electronics and optoelectronics. The designing principles demonstrated in identifying these two tantalizing examples have great potential to accelerate the finding of new functional 2D materials.

Grain-boundary-enhanced carrier collection in CdTe solar cells.

When CdTe solar cells are doped with Cl, the grain boundaries no longer act as recombination centers but actively contribute to carrier collection efficiency. The physical origin of this remarkable effect has been determined through a combination of aberration-corrected scanning transmission electron microscopy, electron energy loss spectroscopy, and first-principles theory. Cl substitutes for a large proportion of the Te atoms within a few unit cells of the grain boundaries. Density functional calculations reveal the mechanism, and further indicate the grain boundaries are inverted to n type, establishing local p-n junctions which assist electron-hole pair separation. The mechanism is electrostatic, and hence independent of the geometry of the boundary, thereby explaining the universally high collection efficiency of Cl-doped CdTe solar cells.

Machine learning enables the discovery of 2D Invar and anti-Invar monolayers.

Materials demonstrating positive thermal expansion (PTE) or negative thermal expansion (NTE) are quite common, whereas those exhibiting zero thermal expansion (ZTE) are notably scarce. In this work, we identify the mechanical descriptors, namely in-plane tensile stiffness and out-of-plane bending stiffness, that can effectively classify PTE and NTE 2D crystals. By utilizing high throughput calculations and the state-of-the-art symbolic regression method, these descriptors aid in the discovery of ZTE or 2D Invar monolayers with the linear thermal expansion coefficient (LTEC) within  ±2 × 10-6 K-1 in the middle range of temperatures. Additionally, the descriptors assist the discovery of large PTE and NTE 2D monolayers with the LTEC larger than  ±15 × 10-6 K-1, which are so-called 2D anti-Invar monolayers. Advancing our understanding of materials with exceptionally low or high thermal expansion is of substantial scientific and technological interest, particularly in the development of next-generation electronics at the nanometer or even Ångstrom scale.

An approach for full space inverse materials design by combining universal machine learning potential, universal property model, and optimization algorithm.

We present a full space inverse materials design (FSIMD) approach that fully automates the materials design for target physical properties without the need to provide the atomic composition, chemical stoichiometry, and crystal structure in advance. Here, we used density functional theory reference data to train a universal machine learning potential (UPot) and transfer learning to train a universal bulk modulus model (UBmod). Both UPot and UBmod were able to cover materials systems composed of any element among 42 elements. Interfaced with optimization algorithm and enhanced sampling, the FSIMD approach is applied to find the materials with the largest cohesive energy and the largest bulk modulus, respectively. NaCl-type ZrC was found to be the material with the largest cohesive energy. For bulk modulus, diamond was identified to have the largest value. The FSIMD approach is also applied to design materials with other multi-objective properties with accuracy limited principally by the amount, reliability, and diversity of the training data. The FSIMD approach provides a new way for inverse materials design with other functional properties for practical applications.

Understanding Defects in Perovskite Solar Cells through Computation: Current Knowledge and Future Challenge.

Lead halide perovskites with superior optoelectrical properties are emerging as a class of excellent materials for applications in solar cells and light-emitting devices. However, perovskite films often exhibit abundant intrinsic defects, which can limit the efficiency of perovskite-based optoelectronic devices by acting as carrier recombination centers. Thus, an understanding of defect chemistry in lead halide perovskites assumes a prominent role in further advancing the exploitation of perovskites, which, to a large extent, is performed by relying on first-principles calculations. However, the complex defect structure, strong anharmonicity, and soft lattice of lead halide perovskites pose challenges to defect studies. In this perspective, on the basis of briefly reviewing the current knowledge concerning computational studies on defects, this work concentrates on addressing the unsolved problems and proposing possible research directions in future. This perspective particularly emphasizes the indispensability of developing advanced approaches for deeply understanding the nature of defects and conducting data-driven defect research for designing reasonable strategies to further improve the performance of perovskite applications. Finally, this work highlights that theoretical studies should pay more attention to establishing close and clear links with experimental investigations to provide useful insights to the scientific and industrial communities.

Graphene-skinned alumina fiber fabricated through metalloid-catalytic graphene CVD growth on nonmetallic substrate and its mass production.

Graphene growth on widely used dielectrics/insulators via chemical vapor deposition (CVD) is a strategy toward transfer-free applications of CVD graphene for the realization of advanced composite materials. Here, we develop graphene-skinned alumina fibers/fabrics (GAFs/GAFFs) through graphene CVD growth on commercial alumina fibers/fabrics (AFs/AFFs). We reveal a vapor-surface-solid growth model on a non-metallic substrate, which is distinct from the well-established vapor-solid model on conventional non-catalytic non-metallic substrates, but bears a closer resemblance to that observed on catalytic metallic substrates. The metalloid-catalytic growth of graphene on AFs/AFFs resulted in reduced growth temperature (~200 °C lower) and accelerated growth rate (~3.4 times faster) compared to that obtained on a representative non-metallic counterpart, quartz fiber. The fabricated GAFF features a wide-range tunable electrical conductivity (1-15000 Ω sq-1), high tensile strength (>1.5 GPa), lightweight, flexibility, and a hierarchical macrostructure. These attributes are inherited from both graphene and AFF, making GAFF promising for various applications including electrical heating and electromagnetic interference shielding. Beyond laboratory level preparation, the stable mass production of large-scale GAFF has been achieved through a home-made roll-to-roll system with capacity of 468-93600 m2/year depending on product specifications, providing foundations for the subsequent industrialization of this material, enabling its widespread adoption in various industries.

Overcoming the Incompatibility Between Electrical Conductivity and Electromagnetic Transmissivity: A Graphene Glass Fiber Fabric Design Strategy.

Conventional conductive materials such as metals are crucial functional components of conductive systems in diverse electronic instruments. However, their severe intrinsic impedance mismatch with air dielectric causes strong reflection of incident electromagnetic waves, and the resulting low electromagnetic transmissivity typically interferes with surrounding electromagnetic signal communications in modern multifunction-integrated instruments. Herein, graphene glass fiber fabric (GGFF) that merges intrinsic electrical and electromagnetic properties of graphene with dielectric attributes and highly porous macrostructure of glass fiber fabric (GFF) is innovatively developed. Using a novel decoupling chemical vapor deposition growth strategy, high-quality and layer-limited graphene is prepared on noncatalytic nonmetallic GFF in a controlled manner; this is pivotal to realizing GGFF with the desired compatibility among high conductivity, low electromagnetic reflectivity, and high electromagnetic transmissivity. At the same sheet resistance over a wide range of values (250-3000 Ω·sq-1), the GGFF exhibits significantly lower electromagnetic reflectivity (by 0.42-0.51) and higher transmissivity (by 0.27-0.62) than those of its metal-based conductive counterpart (CuGFF). The material design strategy reported herein provides a constructive solution to eliminate the incompatibility between electrical conductivity and electromagnetic transmissivity faced by conventional conductive materials, spotlighting the applicability of GGFF in electric heating scenarios in radar, antenna, and stealth systems.

Multispecies-coadsorption-induced rapid preparation of graphene glass fiber fabric and applications in flexible pressure sensor.

Direct chemical vapor deposition (CVD) growth of graphene on dielectric/insulating materials is a promising strategy for subsequent transfer-free applications of graphene. However, graphene growth on noncatalytic substrates is faced with thorny issues, especially the limited growth rate, which severely hinders mass production and practical applications. Herein, graphene glass fiber fabric (GGFF) is developed by graphene CVD growth on glass fiber fabric. Dichloromethane is applied as a carbon precursor to accelerate graphene growth, which has a low decomposition energy barrier, and more importantly, the produced high-electronegativity Cl radical can enhance adsorption of active carbon species by Cl-CH2 coadsorption and facilitate H detachment from graphene edges. Consequently, the growth rate is increased by ~3 orders of magnitude and carbon utilization by ~960-fold, compared with conventional methane precursor. The advantageous hierarchical conductive configuration of lightweight, flexible GGFF makes it an ultrasensitive pressure sensor for human motion and physiological monitoring, such as pulse and vocal signals.

Carrier separation at dislocation pairs in CdTe.

Through the use of aberration corrected scanning transmission electron microscopy, the atomic configuration of CdTe intragrain Shockley partial dislocation pairs has been determined: Single Cd and Te columns are present at opposite ends of both intrinsic and extrinsic stacking faults. These columns have threefold and fivefold coordination, indicating the presence of dangling bonds. Counterintuitively, density-functional theory calculations show that these dislocation cores do not act as recombination centers; instead, they lead to local band bending that separates electrons and holes and reduces undesirable carrier recombination.

Control of one-dimensional magnetism in graphene via spontaneous hydrogenation of the grain boundary.

We propose that control of one-dimensional (1D) magnetism in graphene could be made easier by spontaneous hydrogenation of chemically reactive grain boundaries (GBs) in polycrystalline graphenes. Unlike pristine graphene, where hydrogen adsorption favors the formation of zero-dimensional (0D) clusters, the defect cores (pentagon, heptagon and octagon) at the GBs in polycrystalline graphene promote hydrogenation along the GBs. The hydrogenation in polycrystalline graphene starts at the GBs, proceeds gradually towards the grain interior (GI) and results in smooth 1D graphane-graphene interfaces. Our calculations show that the type (ferro- or antiferro-magnetism) and strength of the magnetism can be controlled by controlling the orientation of GBs. Since GBs in single-layer graphenes can be fabricated in a controllable way in experiments, the hydrogenation of GBs could be a unique method to realize large-area magnetic graphenes for future spintronic applications.

Interphase Boundaries and Their Impact on Carrier Dynamics in Lead Halide Perovskites.

Interphase boundaries (IBs) are widely present in lead halide perovskites (LHPs) owing to their relatively low phase transition barriers. However, their atomic structures and electronic properties have rarely been investigated. In this study, various IB structures were constructed computationally, and their influences on the charge carrier transport properties of LHPs were studied by calculating the effective interphase boundary energy and analyzing the electronic structure. The results show that the presence of IBs plays a significant role in carrier transport and that they may be tuned to prolong carrier lifetimes. This study provides insights for improving the performance of LHPs by engineering IBs, primarily by their compositional phases and ratios.

Distilling universal activity descriptors for perovskite catalysts from multiple data sources via multi-task symbolic regression.

Developing activity descriptors via data-driven machine learning (ML) methods can speed up the design of highly active and low-cost electrocatalysts. Despite the fact that a large amount of activity data for electrocatalysts is stored in the literature, data from different publications are not comparable due to different experimental or computational conditions. In this work, an interpretable ML method, multi-task symbolic regression, was adopted to learn from data in multiple experiments. A universal activity descriptor to evaluate the oxygen evolution reaction (OER) performance of oxide perovskites free of calculations or experiments was constructed and reached high accuracy and generalization ability. Utilizing this descriptor with Bayesian-optimized parameters, a series of compelling double perovskites with excellent OER activity were predicted and further evaluated using first-principles calculations. Finally, the two ML-predicted nickel-based perovskites with the best OER activity were successfully synthesized and characterized experimentally. This work opens a new way to extend machine-learning material design by utilizing multiple data sources.

Manipulation of Shallow-Trap States in Halide Double Perovskite Enables Real-Time Radiation Dosimetry.

Storage phosphors displaying defect emissions are indispensable in technologically advanced radiation dosimeters. The current dosimeter is limited to the passive detection mode, where ionizing radiation-induced deep-trap defects must be activated by external stimulation such as light or heat. Herein, we designed a new type of shallow-trap storage phosphor by controlling the dopant amounts of Ag+ and Bi3+ in the host lattice of Cs2NaInCl6. A distinct phenomenon of X-ray-induced emission (XIE) is observed for the first time in an intrinsically nonemissive perovskite. The intensity of XIE exhibits a quantitative relationship with the accumulated dose, enabling a real-time radiation dosimeter. Thermoluminescence and in situ X-ray photoelectron spectroscopy verify that the emission originates from the radiative recombination of electrons and holes associated with X-ray-induced traps. Theoretical calculations reveal the evolution process of Cl-Cl dimers serving as hole trap states. Analysis of temperature-dependent radioluminescence spectra provides evidence that the intrinsic electron-phonon interaction in 0.005 Ag+@ Cs2NaInCl6 is significantly reduced under X-ray irradiation. Moreover, 0.025 Bi3+@ Cs2NaInCl6 shows an elevated sensitivity to the accumulated dose with a broad response range from 0.08 to 45.05 Gy. This work discloses defect manipulation in halide double perovskites, giving rise to distinct shallow-trap storage phosphors that bridge traditional deep-trap storage phosphors and scintillators and enabling a brand-new type of material for real-time radiation dosimetry.