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.

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.

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.

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.

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.

Fast synthesis of large-area bilayer graphene film on Cu.

Bilayer graphene (BLG) is intriguing for its unique properties and potential applications in electronics, photonics, and mechanics. However, the chemical vapor deposition synthesis of large-area high-quality bilayer graphene on Cu is suffering from a low growth rate and limited bilayer coverage. Herein, we demonstrate the fast synthesis of meter-sized bilayer graphene film on commercial polycrystalline Cu foils by introducing trace CO2 during high-temperature growth. Continuous bilayer graphene with a high ratio of AB-stacking structure can be obtained within 20 min, which exhibits enhanced mechanical strength, uniform transmittance, and low sheet resistance in large area. Moreover, 96 and 100% AB-stacking structures were achieved in bilayer graphene grown on single-crystal Cu(111) foil and ultraflat single-crystal Cu(111)/sapphire substrates, respectively. The AB-stacking bilayer graphene exhibits tunable bandgap and performs well in photodetection. This work provides important insights into the growth mechanism and the mass production of large-area high-quality BLG on Cu.

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.

Transfer-Free Quasi-Suspended Graphene Grown on a Si Wafer.

The direct growth of graphene affording wafer-scale uniformity on insulators is paramount to electronic and optoelectronic applications; however, it remains a challenge to date, because it entails an entirely different growth mode than that over metals. Herein, the metal-catalyst-free growth of quasi-suspended graphene on a Si wafer is demonstrated using an interface-decoupling chemical vapor deposition strategy. The employment of lower-than-conventional H2 dosage and concurrent introduction of methanol during growth can effectively weaken the interaction between the synthesized graphene and the underlying substrate. The growth mode can be thus fine-tuned, producing a predominantly monolayer graphene film with wafer-level homogeneity. Graphene thus grown on a 4 inch Si wafer enables the transfer-free fabrication of high-performance graphene-based field-effect transistor arrays that exhibit almost no shift in the charge neutral point, indicating a quasi-suspended feature of the graphene. Moreover, a carrier mobility up to 15 000 cm2 V-1 s-1 can be attained. This study is anticipated to offer meaningful insights into the synthesis of wafer-scale high-quality graphene on dielectrics for practical graphene devices.

Revisiting the Iodine Vacancy Surface Defects to Rationalize Passivation Strategies in Perovskite Solar Cells.

Current knowledge on the nature of surface iodine vacancies (VI), which are important for the photovoltaic performance and stability of perovskite solar cells, is debatable. We investigated VI on a stable MAI-terminated CH3NH3PbI3 (MAPbI3) surface. First-principles calculations indicated the sensitivity of the atomic structure of surface VI to the charge states and locations on the surface layer. VI in the outermost layer are benign; however, those near the surface can be detrimental. Illumination can promote the diffusion of VI from the outermost layer into the bulk, making them detrimental. There are two mechanisms for the surface passivation of VI: (i) passivation in the second layer to eliminate deep-state VI and (ii) passivation in the outermost layer to inhibit VI diffusion upon illumination (working condition of solar cells). This work rationalizes contradictory reports on the surface properties of halide perovskites and proposes insights into their surface passivation to fabricate high-performing solar cells.

Unique Photoelectric Properties and Defect Tolerance of Lead-Free Perovskite Cs3Cu2I5 with Highly Efficient Blue Emission.

The lead-free copper-based halide perovskite Cs3Cu2I5 is a promising material that can overcome the toxicity and instability of lead-based halide perovskites, thereby affording remarkable performance in the field of optoelectronics. Cs3Cu2I5 perovskite exhibits blue emission with a very high photoluminescence quantum yield (PLQY). First-principles calculations were used herein to theoretically expound the origins of the high PLQY of Cs3Cu2I5: (i) the low symmetry of Cs3Cu2I5 breaks the forbidden transition and enables the transition process; (ii) the large transition matrix and high transition rate increase the probability for radiative recombination of Cs3Cu2I5; (iii) the good defect tolerance broadens the path for thermal relaxation and radiative recombination. The high transition rate and good defect tolerance account for the high-efficiency PLQY of the lead-free copper-based perovskite, Cs3Cu2I5.

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.

Crystal structure prediction by combining graph network and optimization algorithm.

Crystal structure prediction is a long-standing challenge in condensed matter and chemical science. Here we report a machine-learning approach for crystal structure prediction, in which a graph network (GN) is employed to establish a correlation model between the crystal structure and formation enthalpies at the given database, and an optimization algorithm (OA) is used to accelerate the search for crystal structure with lowest formation enthalpy. The framework of the utilized approach (a database + a GN model + an optimization algorithm) is flexible. We implemented two benchmark databases, i.e., the open quantum materials database (OQMD) and Matbench (MatB), and three OAs, i.e., random searching (RAS), particle-swarm optimization (PSO) and Bayesian optimization (BO), that can predict crystal structures at a given number of atoms in a periodic cell. The comparative studies show that the GN model trained on MatB combined with BO, i.e., GN(MatB)-BO, exhibit the best performance for predicting crystal structures of 29 typical compounds with a computational cost three orders of magnitude less than that required for conventional approaches screening structures through density functional theory calculation. The flexible framework in combination with a materials database, a graph network, and an optimization algorithm may open new avenues for data-driven crystal structural predictions.

Interpretation of Rubidium-Based Perovskite Recipes toward Electronic Passivation and Ion-Diffusion Mitigation.

Rubidium cation (Rb+ ) addition is witnessed to play a pivotal role in boosting the comprehensive performance of organic-inorganic hybrid perovskite solar cells. However, the origin of such success derived from irreplaceable superiorities brought by Rb+ remains ambiguous. Herein, grain-boundary-including atomic models are adopted for the accurate theoretical analysis of practical Rb+ distribution in perovskite structures. The spatial distribution, covering both the grain interiors and boundaries, is thoroughly identified by virtue of synchrotron-based grazing-incidence X-ray diffraction. On this basis, the prominent elevation of the halogen vacancy formation energy, improved charge-carrier dynamics, and the electronic passivation mechanism in the grain interior are expounded. As evidenced by the increased energy barrier and suppressed microcurrent, the critical role of Rb+ addition in blocking the diffusion pathway along grain boundaries, inhibiting halide phase segregation, and eventually enhancing intrinsic stability is elucidated. Hence, the linkage avalanche effect of occupied location dominated by subtle changes in Rb+ concentration on electronic defects, ion migration, and phase stability is completely investigated in detail, shedding a new light on the advancement of high-efficiency cascade-incorporating strategies and perovskite compositional engineering.

Photoinduced Dynamic Defects Responsible for the Giant, Reversible, and Bidirectional Light-Soaking Effect in Perovskite Solar Cells.

Perovskite solar cells (PSCs) exhibit large, reversible, and bidirectional light-soaking effects (LSEs); however, these anomalous LSEs are poorly understood, limiting the stability engineering and commercialization. We present a unified defect theory for the LSEs in lead halide perovskites by reconciling their defect photochemistry, ionic migration, and carrier dynamics. We considered typical detrimental defects (IPb, Ii, VI) and observed that two atomic configurations were favored, where the carrier lifetime of one configuration was nearly 1 order of magnitude longer than that in the other. First-principles calculations showed that light illumination promotes ion-diffusion-assisted transitions from energetically stable configurations to metastable configurations, which are converted back to stable configurations in the dark. Fermi-level-dependent formation energies of stable/metastable configurations were used to rationalize contradictory experimental results of anomalous LSEs in PSCs observed in various studies, thus providing insights for minimizing the LSE to achieve high-performance stable PSCs.

A chalcogenide-cluster-based semiconducting nanotube array with oriented photoconductive behavior.

The interesting physical and chemical properties of carbon nanotubes (CNTs) have prompted the search for diverse inorganic nanotubes with different compositions to expand the number of available nanotechnology applications. Among these materials, crystalline inorganic nanotubes with well-defined structures and uniform sizes are suitable for understanding structure-activity relationships. However, their preparation comes with large synthetic challenges owing to their inherent complexity. Herein, we report the example of a crystalline nanotube array based on a supertetrahedral chalcogenide cluster, K3[K(Cu2Ge3Se9)(H2O)] (1). To the best of our knowledge, this nanotube array possesses the largest diameter of crystalline inorganic nanotubes reported to date and exhibits an excellent structure-dependent electric conductivity and an oriented photoconductive behavior. This work represents a significant breakthrough both in terms of the structure of cluster-based metal chalcogenides and in the conductivity of crystalline nanotube arrays (i.e., an enhancement of ~4 orders of magnitude).

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.

High-Performance Blue Perovskite Light-Emitting Diodes Enabled by Efficient Energy Transfer between Coupled Quasi-2D Perovskite Layers.

While there has been extensive investigation into modulating quasi-2D perovskite compositions in light-emitting diodes (LEDs) for promoting their electroluminescence, very few reports have studied approaches involving enhancement of the energy transfer between quasi-2D perovskite layers of the film, which plays very important role for achieving high-performance perovskite LEDs (PeLEDs). In this work, a bifunctional ligand of 4-(2-aminoethyl)benzoic acid (ABA) cation is strategically introduced into the perovskite to diminish the weak van der Waals gap between individual perovskite layers for promoting coupled quasi-2D perovskite layers. In particular, the strengthened interaction between coupled quasi-2D perovskite layers favors an efficient energy transfer in the perovskite films. The introduced ABA can also simultaneously passivate the perovskite defects by reducing metallic Pb for less nonradiative recombination loss. Benefiting from the advanced properties of ABA incorporated perovskites, highly efficient blue PeLEDs with external quantum efficiency of 10.11% and a very long operational stability of 81.3 min, among the best performing blue quasi-2D PeLEDs, are achieved. Consequently, this work contributes an effective approach for high-performance and stable blue PeLEDs toward practical applications.

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.

Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts.

Symbolic regression (SR) is an approach of interpretable machine learning for building mathematical formulas that best fit certain datasets. In this work, SR is used to guide the design of new oxide perovskite catalysts with improved oxygen evolution reaction (OER) activities. A simple descriptor, µ/t, where µ and t are the octahedral and tolerance factors, respectively, is identified, which accelerates the discovery of a series of new oxide perovskite catalysts with improved OER activity. We successfully synthesise five new oxide perovskites and characterise their OER activities. Remarkably, four of them, Cs0.4La0.6Mn0.25Co0.75O3, Cs0.3La0.7NiO3, SrNi0.75Co0.25O3, and Sr0.25Ba0.75NiO3, are among the oxide perovskite catalysts with the highest intrinsic activities. Our results demonstrate the potential of SR for accelerating the data-driven design and discovery of new materials with improved properties.