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To reliably deploy lithium-ion batteries, a fundamental understanding of cycling and aging behavior is critical. Battery aging, however, consists of complex and highly coupled phenomena, making it challenging to develop a holistic interpretation. In this work, we generate a diverse battery cycling dataset with a broad range of degradation trajectories, consisting of 363 high energy density commercial Li(Ni,Co,Al)O$_2$/Graphite + SiO$_x$ cylindrical 21700 cells cycled under 218 unique cycling protocols. We consolidate aging via 16 mechanistic state-of-health (SOH) metrics, including cell-level performance metrics, electrode-specific capacities/state-of-charges (SOCs), and aging trajectory descriptors. Through the use of interpretable machine learning and explainable features, we deconvolute the underlying factors that contribute to battery degradation. This generalizable data-driven framework reveals the complex interplay between cycling conditions, degradation modes, and SOH, representing a holistic approach towards understanding battery aging.

Exploratory synthesis has been the main generator of new inorganic materials for decades. However, our Edisonian and bias-prone processes of synthetic exploration alone are no longer sufficient in an age that demands rapid advances in materials development. In this work, we demonstrate one of the first end-to-end attempts towards systematic, computer-aided discovery and laboratory synthesis of inorganic crystalline compounds as a modern alternative to purely exploratory synthesis. Our approach initializes materials discovery campaigns by autonomously mapping the synthetic feasibility of a chemical system using density functional theory with AI feedback. Following expert-driven down-selection of newly generated phases, we use solid-state synthesis and in situ characterization via hot-stage X-ray diffraction in order to realize new ternary oxide phases experimentally. We applied this strategy in six ternary transition-metal oxide chemistries previously considered well-explored, one of which culminated in the discovery of two novel phases of calcium ruthenates. Detailed characterization using room temperature X-ray powder diffraction, 4D-STEM and SQUID measurements identify the structure, composition and confirm distinct properties, including distinct defect concentrations, of one of the new phases formed in our experimental campaigns. While the discovery of a new material guided by AI and DFT theory represents a milestone, our procedure and results also highlight a number of critical gaps in the process that can inform future efforts towards the improvement of AI-coupled methodologies, which are discussed.

Lithium-ion batteries (LIBs) have attracted widespread attention as an efficient energy storage device on electric vehicles (EV) to achieve emission-free mobility. However, the performance of LIBs deteriorates with time and usage, and the state of health of used batteries are difficult to quantify and to date are poorly understood. Having accurate estimations of a battery's remaining life across different life stages would benefit maintenance, safety, and serve as a means of qualifying used batteries for second-life applications. Since the full history of a battery may not always be available in downstream applications, in this study, we demonstrate a deep learning framework that enables dynamic degradation trajectory prediction, while requiring only the most recent battery usage information. Specifically, our model takes a rolling window of current and voltage time-series inputs, and predicts the near-term and long-term capacity fade via a recurrent neural network. We exhaustively benchmark our model against a naive extrapolating model by evaluating the error on reconstructing the discharge capacity profile under different settings. We show that our model's performance in accurately inferring the battery's degradation profile is "agnostic" with respect to cell cycling history and its current state of health.

Perovskite-related materials show very promising properties in many fields. Pb-free perovskites are particularly interesting, because of the toxicity of Pb. In this study, hybrid double perovskite MA

This study is motivated by the desire to disseminate a low-cost, high-precision, high-throughput environmental chamber to test materials and devices under elevated humidity, temperature, and light. This paper documents the creation of an open-source tool with a bill of materials as low as US$2,000, and the subsequent evolution of three second-generation tools installed at three different universities spanning thin films, bulk crystals, and thin-film solar-cell devices. We introduce an optical proxy measurement to detect real-time phase changes in materials. We present correlations between this optical proxy and standard X-ray diffraction measurements, describe some edge cases where the proxy measurement fails, and report key learnings from the technology-translation process. By sharing lessons learned, we hope that future open-hardware development and translation efforts can proceed with reduced friction. Throughout the paper, we provide examples of scientific impact, wherein participating laboratories used their environmental chambers to study and improve the stabilities of halide-perovskite materials. All generations of hardware bills of materials, assembly instructions, and operating codes are available in open-source repositories.

Sepsis is recognized as an inflammation-related syndrome in response to invading pathogens. Many patients suffer from sepsis including transplant recipients. Lipopolysaccharide (LPS) is known to trigger sepsis-related organ dysfunction. This study expounded on the possible effect of microRNA (miR)-338-3p in sepsis-induced acute lung injury (ALI). Firstly, human bronchial epithelial cell line 16HBE received LPS treatment to establish the cell models of sepsis-induced ALI. The expression patterns of miR-338-3p, long non-coding RNA OPA-interacting protein 5 antisense transcript 1 (lncRNA OIP5-AS1), and activating transcription factor 4 (ATF4) in 16HBE cells were examined. Afterwards, 16HBE cell viability, the apoptosis rate, and the levels of inflammation and lactate dehydrogenase (LDH) were determined to assess the degree of cell injury. We disclosed that LPS treatment triggered 16HBE cell injury, downregulated miR-338-3p, and upregulated OIP5-AS1 and ATF4. miR-338-3p overexpression repressed LPS-induced 16HBE cell injury. miR-338-3p diminished OIP5-AS1 stability via binding to OIP5-AS1 and downregulated OIP5-AS1 expression and OIP5-AS1 can enhance ATF4 mRNA stability and upregulate ATF4 mRNA level. The rescue experiments showed that ATF4 overexpression aggravated LPS-induced 16HBE cell injury. Overall, miR-338-3p overexpression decreased OIP5-AS1 expression and stability and further downregulated ATF4 mRNA level, thereby mitigating LPS-induced 16HBE cell injury.

Summary Search for resource-efficient materials in vast compositional spaces is an outstanding challenge in creating environmentally stable perovskite semiconductors. We demonstrate a physics-constrained sequential learning framework to subsequently identify the most stable alloyed organic-inorganic perovskites. We fuse data from high-throughput degradation tests and first-principle calculations of phase thermodynamics into an end-to-end Bayesian optimization algorithm using probabilistic constraints. By sampling just 1.8% of the discretized CsxMAyFA1−x−yPbI3 (MA, methylammonium; FA, formamidinium) compositional space, perovskites centered at Cs0.17MA0.03FA0.80PbI3 show minimal optical change under increased temperature, moisture, and illumination with >17-fold stability improvement over MAPbI3. The thin films have 3-fold improved stability compared with state-of-the-art multi-halide Cs0.05(MA0.17FA0.83)0.95Pb(I0.83Br0.17)3, translating into enhanced solar cell stability without compromising conversion efficiency. Synchrotron-based X-ray scattering validates the suppression of chemical decomposition and minority phase formation achieved using fewer elements and a maximum of 8% MA. We anticipate that this data fusion approach can be extended to guide materials discovery for a wide range of multinary systems.

Incorporating a low dimensional LD perovskite capping layer on top of a perovskite absorber, improves the stability of perovskite solar cells PSCs . However, in the case of mixed halide perovskites, which can undergo halide segregation into single halide perovskites, a systematic study of the capping layer s effect on mixed halide perovskite absorber is still lacking. This study bridges this gap by investigating how the 1D perovskite capping layers on top of MAPb IxBr1 amp; 8722;x 3 x 0, 0.25, 0.5, 0.75, 1 absorbers affect the films stability. We utilize a new method, dissimilarity matrix, to investigate the image based stability performance of capping absorber pair compositions across time. This method overcomes the challenge of analyzing various film colors due to bandgap difference in mixed halide perovskites. We also discover that the intrinsic absorber stability plays an important role in the overall stability outcome, despite the capping layer s support. Within the 55 unique capping absorber pairs, we observe a notable 1D perovskite material, 1 methoxynaphthalene 2 ethylammonium chloride 2MeO NEA Cl or 9 Cl , that improves the stability of MAPbI3 and MAPb I0.5Br0.5 3 by at least 8 and 1.5 times, respectively, compared to bare films under elevated humidity and temperature. Surface photovoltage results also show that the accumulation of electrostatic charges on the film surface depends on the capping layer type, which could contribute to the acceleration deceleration of degradation

The volatile organic compounds (VOCs) emitted from wood-based panels are hazardous to indoor air quality. Usually, the VOCs are derived from the adhesive, chemical compounds, and wood components. However, there has been little research focusing on the effects of manufacture conditions on the VOC emissions. In this study, the effects of density, thickness, and resin content on total VOC (TVOC) and individual VOCs were investigated by the small chamber method and gas chromatography and mass spectrometry (GC/MS). The TVOC emission from the particleboard of each manufacturing condition decreased with extended exposure time. The higher density, thickness, and resin content of particleboard at each measured time caused higher concentrations of TVOC emissions. Most of the detected VOCs were aromatics. The esters, aldehydes, and ketones showed a high increasing level with increasing particleboard density, thickness, and resin content. This result indicated that these chemical compounds were most sensitive to changes in manufacturing conditions.

Doping is an effective strategy for tuning metal oxide-based semiconductors for solar-driven photoelectrochemical (PEC) water splitting. Despite decades of extensive research effort, the dopant selection is still largely dependent on a trial-and-error approach. Machine learning (ML) is promising in providing predictable insights on the dopant selection for high-performing PEC systems because it can uncover correlations from the seemingly ambiguous linkages between vast features of dopants and the PEC performance of doped photoelectrodes. Herein, the authors successfully build ML model to predict the doping effect of 17 metal dopants into hematite (Fe

Halide perovskite materials have attracted great interest for applications in low-cost, solution-processed solar cells and other optoelectronics applications. The role of moisture in perovskite device degradation and crystal formation processes remains poorly understood. Here, we use a data-driven approach to discover the influence of trace amounts of water on perovskite crystal formation by analyzing a comprehensive dataset of 8470 inverse-temperature crystallization lead iodide perovskite synthesis reactions, performed over 20 months using a robotic system. We identified discrepancies between the empirical crystal formation rates in batches of experiments conducted under different ambient relative humidity conditions for each organoammonium cation. We prioritized these using a statistical model and then used the robotic system to conduct 1296 controlled interventional experiments, in which small amounts of water were deliberately introduced to the reactions. The addition of trace amounts of water promotes crystal formation for 4-methoxyphenylammonium lead iodide and iso-propylammonium lead iodide and inhibits crystal formation for dimethylammonium lead iodide and acetamidinium lead iodide. We also performed thin-film syntheses of these four materials and determined the grain size distributions using scanning electron microscopy. The addition of water results in smaller grain sizes for dimethylammonium and larger grain sizes for iso-propylammonium, consistent with earlier or delayed nucleation, respectively. The agreement between the inverse temperature crystallization and thin film results indicates that this is a feature of the organoammonium-water interaction that persists despite differences in the synthesis method.

Hybrid organic-inorganic perovskites (HOIPs) have attracted significant attention in recent years attributed to their outstanding physical properties and striking application potentials in the fields of photovoltaic and optoelectronic devices. For the commercialization of devices employing hybrid perovskite materials, their mechanical properties critically influence the manufacturing, processing as well as the device durability, and hence need to be well understood. In this review, we summarize recent mechanical studies of the known HOIPs. Firstly, we present a comprehensive account of the relationship between crystal structures and properties, where the influence of different chemical and bonding factors on the load response of HOIPs are extensively discussed. The functional abnormalities of HOIP materials under the stress stimuli are also reviewed, addressing the effects of negative linear compressibility, negative Poisson’s ratio and barocaloric effect. Finally, we briefly summarize the significant effect of strain and stress in thin-film halide perovskite materials, which have been widely employed in solar cells and Light-emitting diodes. This review aims to raise more awareness about the fundamental understanding of the mechanical properties of hybrid perovskite materials, and attract more research attention to this interdisciplinary field.

Summary Accelerating the experimental cycle for new materials development is vital for addressing the grand energy challenges of the 21st century. We fabricate and characterize 75 unique perovskite-inspired compositions within a 2-month period, with 87% exhibiting band gaps between 1.2 and 2.4 eV, which are of interest for energy-harvesting applications. We utilize a fully connected deep neural network to classify compounds based on experimental X-ray diffraction data into 0D, 2D, and 3D structures, more than 10 times faster than human analysis and with 90% accuracy. We validate our methods using lead-halide perovskites and extend the application to lead-free compositions. The wider synthesis window and faster cycle of learning enables the realization of a multi-site lead-free alloy series, Cs3(Bi1-xSbx)2(I1-xBrx)9. We reveal the non-linear band-gap behavior and transition in dimensionality upon simultaneous alloying on the B-site and X-site of Cs3Bi2I9 with Sb and Br.

The Brucella spp encounter stressful environment inside their host cells. The Lon protein is an important protease related to cellular protein degradation and resistance to stress in Brucella. However, the molecular mechanism between Lon protein and stress response was still unknown. In this study, it was found that the lon mutant exhibited obvious growth defect in TSB medium, compared with its parent strain. In addition, our results indicated that Lon protein was involved in resistance to various stress conditions and all the β-lactam antibiotics tested. Although deletion of this protease did not affect Brucella virulence in macrophage, the mutant strain was significantly attenuated in mice infection model at 1 week post infection, and the expression level of several cytokine genes was significantly changed in vivo. To gain insight into the genetic basis for the distinctive phenotypic properties exhibited by the lon mutant strain, RNA-seq was performed, and the result showed that various genes involved in stress response, quorum sensing and transcriptional regulation were significantly altered in Δlon strain. Overall, these studies have preliminary uncovered the molecular mechanism between Lon protease, stress response and bacterial virulence.

The mechanism and elemental composition that form the basis for the improved optical and electronic properties in mixed-ion lead halide perovskite solar cells are still not well understood compared...

The role electron-transport layers (ETLs) play in perovskite solar cells (PSCs) is still widely debated. Conduction band alignment at the perovskite/ETL interface has been suggested to be an import...

Pure metal-organic framework (MOF) layers deposited on porous supports are important candidates for molecular sieving membranes, but their performance usually deviates from theoretical estimations. Here, we combine step-wise scanning electron microscopy imaging, time-resolved synchrotron X-ray scattering, terahertz infrared spectroscopy, and density functional theory calculation to investigate the ZIF-8 membrane formation on two types (polydopamine and TiO2) of functionalized porous supports. Though molecular sieving of ZIF-8 membranes for smaller gases (He, H2, and CO2) can be achieved with both types of functionalized supports, we unravel that the strong interaction between MOF and polydopamine can disrupt the formation of "perfect" MOF crystals at the interface, leading to a "contracted" MOF structure with partially uncoordinated imidazolate ligands. This further affects the low-frequency dynamical parameters of the framework and inhibits the effective seeded growth. Eventually, it leads to an unexpected loss of selectivity for the bulkier gases (N2 and CH4) for ZIF-8 on polydopamine-functionalized supports. This work links the dynamical aspects of MOFs with their gas transport behavior and highlights the importance of regulating the interfacial weak forces to preserve the ideal molecular sieving efficiency of MOF membranes, which also provides guidance for defect engineering of MOF film fabrication for sensing and electronic devices beyond membranes.

In a search for Pb-free photovoltaic materials, a double perovskite Cs2AgSbBr6 with an indirect optical bandgap of 1.64 eV has been synthesized. Single crystal X-ray diffraction determined the space group as Fmm with a = 11.1583(7) A. The black, as-synthesised compound turned brown after heat treatment at 480 K while the symmetry and crystallinity were preserved. X-ray photoelectron spectroscopy indicated the existence of Sb5+ in the black crystals, suggesting that the dark colour arises from the Sb3+–Sb5+ charge transfer. Furthermore, UV visible spectroscopy and density functional theory calculations have been applied to probe the optical properties and electronic structure.

Traditionally, the discovery of new solid-state materials with user-defined properties is driven either by human intuition, heuristic chemical rules, and/or density functional theory (DFT). However, these methods have limitations, either in accessibly (domain expertise), accuracy, and/or throughput [1]. Consequently, the material search space remains underexplored, given order 105 reported compounds compared to order 1010 theorized ternary compounds [2]. To accelerate the exploration of new solid-state materials, a framework capable of inverse design for materials with user-defined properties is needed. Herein, we present a generalized framework for inverse design of crystals with user-defined properties, which include both ground-state and excited-state properties (e.g., thermoelectric power factor) using sparsely labelled training data. The key enabler of this inverse-design framework is a general and invertible crystallographic representation that encodes the crystallographic information into the representations in both real space and reciprocal space. The trained surrogate model achieves similar property prediction accuracy and precision as DFT calculations within seconds. Using the developed inverse-design framework, we design 79 new crystals with user-targeted formation energies, 17 crystals with targeted bandgap, and 27 crystals for potential thermoelectric applications. The compositions of those designed materials are unique and cannot be found in the training or test sets. We validate our predictions using first-principle calculations. ­­Toward bridging the gap between simulation and experiment, we demonstrate a naive synthesizability metric — predicting the existence of an ICSD record — and show this methodology can, in principle, include stability and/or synthesizability as a target metric, once consensus metrics are agreed upon by the field.

New antibiotics are needed to battle growing antibiotic resistance, but the development process from hit, to lead, and ultimately to a useful drug takes decades. Although progress in molecular property prediction using machine-learning methods has opened up new pathways for aiding the antibiotics development process, many existing solutions rely on large data sets and finding structural similarities to existing antibiotics. Challenges remain in modeling unconventional antibiotic classes that are drawing increasing research attention. In response, we developed an antimicrobial activity prediction model for conjugated oligoelectrolyte molecules, a new class of antibiotics that lacks extensive prior structure-activity relationship studies. Our approach enables us to predict the minimum inhibitory concentration for E. coli K12, with 21 molecular descriptors selected by recursive elimination from a set of 5305 descriptors. This predictive model achieves an R2 of 0.65 with no prior knowledge of the underlying mechanism. We find the molecular representation optimum for the domain is the key to good predictions of antimicrobial activity. In the case of conjugated oligoelectrolytes, a representation reflecting the three-dimensional shape of the molecules is most critical. Although it is demonstrated with a specific example of conjugated oligoelectrolytes, our proposed approach for creating the predictive model can be readily adapted to other novel antibiotic candidate domains.

Brucellosis, caused by Brucella spp., is an important zoonotic disease leading to enormous economic losses in livestock, posing a great threat to public health worldwide. The live attenuated Brucella suis (B. suis) strain S2, a safe and effective vaccine, is widely used in animals in China. However, S2 vaccination in animals may raise debates and concerns in terms of safety to primates, particularly humans. In this study, we used cynomolgus monkey as an animal model to evaluate the safety of the S2 vaccine strain on primates. In addition, we performed transcriptome analysis to determine gene expression profiling on cynomolgus monkeys immunized with the S2 vaccine. Our results suggested that the S2 vaccine was safe for cynomolgus monkeys. The transcriptome analysis identified 663 differentially expressed genes (DEGs), of which 348 were significantly upregulated and 315 were remarkably downregulated. The Gene Ontology (GO) classification and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that these DEGs were involved in various biological processes (BPs), including the chemokine signaling pathway, actin cytoskeleton regulation, the defense response, immune system processing, and the type-I interferon signaling pathway. The molecular functions of the DEGs were mainly comprised of 2'-5'-oligoadenylate synthetase activity, double-stranded RNA binding, and actin-binding. Moreover, the cellular components of these DEGs included integrin complex, myosin II complex, and blood microparticle. Our findings alleviate the concerns over the safety of the S2 vaccine on primates and provide a genetic basis for the response from a mammalian host following vaccination with the S2 vaccine.

The current problems facing literary criticism are not only the influence of the external environment, but also the understanding of the traditional Chinese culture from the perspective of "globalization" and "diversification" of literature. It is also a guide for related literary criticism. This paper first discusses the pluralism of literary criticism, gives an overview of literary criticism, analyzes the problems of literary criticism in Chinese literature in our country, and finally propoeses relevant suggestions for literary criticism under pluralism.

Our country has a long history and culture. Teachers should actively promote the excellent Chinese traditional culture, do a good job of teaching and guidance, and effectively improve students' learning enthusiasm in the teaching of Chinese language and literature. However, the current Chinese literature taught in vocational higher education institution still has problems such as unclear educational goals, low curriculum status, and low level of informatization. Therefore, this paper fully studies and utilizes the advantages of vocational education on the premise of fully understanding the development goals of Chinese education in vocational higher education institution, enriching the content of Chinese language and literature education, strengthening Chinese language and literature education, and cultivating information ability. It is to help students better understand our country’s fine traditional culture, guide students to establish correct learning concepts, and improve overall quality.

Stability of perovskite-based photovoltaics remains a topic requiring further attention. Cation engineering influences perovskite stability, with the present-day understanding of the impact of cations based on accelerated ageing tests at higher-than-operating temperatures (e.g. 140°C). By coupling high-throughput experimentation with machine learning, we discover a weak correlation between high/low-temperature stability with a stability-reversal behavior. At high ageing temperatures, increasing organic cation (e.g. methylammonium) or decreasing inorganic cation (e.g. cesium) in multi-cation perovskites has detrimental impact on photo/thermal-stability; but below 100°C, the impact is reversed. The underlying mechanism is revealed by calculating the kinetic activation energy in perovskite decomposition. We further identify that incorporating at least 10 mol.% MA and up to 5 mol.% Cs/Rb to maximize the device stability at device-operating temperature (, Current view of the impact of A-site cation on the stability of perovskite materials and devices is derived from accelerated ageing tests at high temperature, which is beyond normal operation range. Here, the authors reveal the great impact of ageing condition on assessing the photothermal stability of mixed-cation perovskites using high-throughput robot system coupled with machine learning.

Compositional search within multinary perovskites employing brute force synthesis are prohibitively expensive in large chemical spaces. To identify the most stable multi-cation lead iodide perovskites containing Cs, formamidinium (FA) and methylammonium (MA), we fuse results from density functional theory (DFT) calculations and in situ thin-film degradation test within an end-to-end machine learning (ML) algorithm to inform the compositional optimization of CsxMAyFA1-x-yPbI3. We integrate phase thermodynamics modelling as a probabilistic constraint in a Bayesian optimization (BO) loop, which effectively guides the experimental search while considering both structural and environmental stability. After three optimization rounds and only sampling 1.8% of the compositional space, we identify thin-film compositions centred at Cs0.17MA0.03FA0.80PbI3 that achieve a 3x delay in macroscopic degradation onset under elevated temperature, humidity, and light compared with the more complex state-of-the-art Cs0.05(MA0.17FA0.83)0.95Pb(I0.83Br0.17)3. We find up to 8% of MA can be incorporated into the perovskite structure before stability is significantly compromised. Cs is beneficial at low concentrations, however, beyond 17% is found to contribute to reduced stability. Synchrotron-based grazing-incidence wide-angle X-ray scattering (GIWAXS) further validates that the interplay of chemical decomposition and phase separation governs the non-linear instability landscape of this compositional space. We reveal the detrimental role of the ẟ-CsPbI3 minority phase in accelerating degradation and it can be kinetically suppressed by co-optimising Cs and MA content, providing insights into simplifying perovskite compositions for further environmental stability enhancement. Our approach realizes the effectiveness of ML-enabled data fusion in achieving a holistic, efficient, and physics-informed experimentation for multinary systems, potentially generalisable to materials search in the vast structural and alloyed spaces beyond halide perovskites.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

After reaching a device efficiency level comparable to silicon, perovskite solar cell's next big challenge is to tackle its environmental instability issue. To solve this problem, researchers have started incorporating a buffer layer called ‘capping layer’, consisting of low dimensional (LD) perovskite, sandwiched between perovskite absorber and hole transport layer. However, there is no conclusive agreement on how to select capping layer material that best extends the stability. By using feature importance rank on the regression models, we can start to see which molecular properties on capping layer have significant impact in suppressing degradation.

We report the complete genome sequence of Mycoplasma bovis strain XBY01, which was isolated from a severely diseased young calf in Henan Province, China, in 2019. The genome of XBY01 contains a single circular chromosome of 986,067 bp, with a GC content of 29.30%.

Environmental stability of perovskite solar cells (PSCs) has been improved by trial-and-error exploration of thin low-dimensional (LD) perovskite deposited on top of the perovskite absorber, called the capping layer. In this study, a machine-learning framework is presented to optimize this layer. We featurize 21 organic halide salts, apply them as capping layers onto methylammonium lead iodide (MAPbI3) films, age them under accelerated conditions, and determine features governing stability using supervised machine learning and Shapley values. We find that organic molecules’ low number of hydrogen-bonding donors and small topological polar surface area correlate with increased MAPbI3 film stability. The top performing organic halide, phenyltriethylammonium iodide (PTEAI), successfully extends the MAPbI3 stability lifetime by 4 ± 2 times over bare MAPbI3 and 1.3 ± 0.3 times over state-of-the-art octylammonium bromide (OABr). Through characterization, we find that this capping layer stabilizes the photoactive layer by changing the surface chemistry and suppressing methylammonium loss., The stability of perovskite solar cells can be improved by using hybrid-organic perovskites capping-layers atop the active material. Here the authors use machine learning to optimize capping layers by monitoring time to degradation of differently capped lead-halide perovskite solar cells.

A key debate involving mixed-cation lead mixed-halide perovskite thin-films relates to the effects of process conditions on film morphology and local performance of perovskite solar cells. In this contribution, we investigate the influence of precursor concentration on the film thickness, grain size, and orientation of these polycrystalline thin-films. We vary the molar concentration of the perovskite precursor containing Rb, Cs, MA, FA, Pb, I, and Br from 0.4 to 1.2 M. We use optical and electrical probes to measure local properties and correlate the effect of crystallographic orientation on the inter- and intragrain charge-carrier transport. We find that, with increasing precursor concentration, the grain size of the polycrystalline thin-films becomes larger and more faceted. Films with small grains show mostly random grain orientation angles, whereas films with large grains are oriented with {100} planes around an angle of 20° relative to the surface normal. These films with oriented large grains also ...

Inspired by the rapid rise in efficiencies of lead halide perovskite (LHP) solar cells, lead-free alternatives are attracting increasing attention. In this work, we study the photovoltaic potential of solution-processed antimony (Sb)-based compounds with the formula A3Sb2I9 (A = Cs, Rb, and K). We experimentally determine bandgap magnitude and type, structure, carrier lifetime, exciton binding energy, film morphology, and photovoltaic device performance. We use density functional theory to compute the equilibrium structures, band structures, carrier effective masses, and phase stability diagrams. We find the A-site cation governs the structural and optoelectronic properties of these compounds. Cs3Sb2I9 has a 0D structure, the largest exciton binding energy (175 ± 9 meV), an indirect bandgap, and, in a solar cell, low photocurrent (0.13 mA cm–2). Rb3Sb2I9 has a 2D structure, a direct bandgap, and, among the materials investigated, the lowest exciton binding energy (101 ± 6 meV) and highest photocurrent (1....

The mechanical properties of formamidinium lead halide perovskites (FAPbX3, X = Br or I) grown by inverse temperature crystallization have been studied by nanoindentation. The measured Young's moduli (9.7 - 12.3 GPa) and hardnesses (0.36 - 0.45 GPa) indicate good mechanical flexibility and ductility. The effects of hydrogen bonding were evaluated by performing ab initio molecular dynamics on both formamidinium and methylammonium perovskites and calculating radial distribution functions. The structural and chemical factors influencing these properties are discussed by comparison with corresponding values in the literature for other hybrid perovskites, including double perovskites. Our results reveal that bonding in the inorganic framework and hydrogen bonding play important roles in determining elastic stiffness. The influence of the organic cation becomes more important for structures at the limit of their perovskite stability, indicated by high tolerance factors.

Based on a new “one for two” strategy, a single two-dimensional (2D) metal–organic framework (MOF) precursor has been transformed into both electrodes (i.e., a Co3O4 cathode and a N-doped carbon anode) for a flexible asymmetric supercapacitor. The device demonstrated not only highly robust mechanical flexibility but also outstanding electrochemical performance. The “one for two” concept can significantly ease the fabrication process and has great potential to be extended to other functional materials for different applications. NRF (Natl Research Foundation, S’pore) MOE (Min. of Education, S’pore) Accepted version

Infectious bursal disease (IBD) is a highly infectious disease in chicken, and vaccination is the best way to prevent outbreak of infectious bursal disease virus (IBDV). In this study, we isolated a variant IBDV strain from a chicken farm with vaccinated chickens. The full genome of this IBDV strain was determined and analyzed.

Brucella spp. are facultative intracellular pathogens and zoonotic agents which pose a huge threat to human health and animal husbandry. The B. melitensis, B. abortus, and B. suis cause undulant fever and influenza-like symptoms in humans. However, the effects of B. canis have not been extensively studied. The quorum sensing-dependent transcriptional regulator VjbR influences the Brucella virulence in smooth type Brucella strains, such as B. melitensis, B. abortus and rough type Brucella ovis. However, the function of VjbR in the rough-type B. canis is unknown. In the present study, we discovered that deletion of this regulator significantly affected Brucella virulence in macrophage and mice infection models. The expression levels of virB operon and the ftcR gene were significantly altered in the vjbR mutant strain. We further investigated the protective effect of different doses of the vjbR mutant in mice and the results indicated that VjbR conferred protection against the virulent B. canis strain. This study presents the first evidence that the transcriptional regulator VjbR has important function in B. canis. In addition, according to its reduced virulence and the protective immunity it induces in mice, it can be a potential live attenuated vaccine against B. canis.

Compositional engineering related to the organic and inorganic cations (A-site) in halide perovskites solar cells has helped improve efficiency and long-term durability. However, this compositional complexity can lead to phase segregation that weakens the optoelectronic performance. Here, we show the halide distribution and cation distribution by means of synchrotron-based nanoprobe x-ray fluorescence. We find that the halide distribution homogenizes upon the addition of CsI and RbI precursors. The halide homogenization coincides with long-lived charge carrier decays. Additionally, we observe Rb-rich areas that phase segregate within the film and the Rb aggregates are identified to be recombination active using X-ray and E-beam induced current microscopy.

This journal is © The Royal Society of Chemistry. Methylammonium lead iodide (MAPI) is a prototypical photoabsorber in perovskite solar cells (PSCs), reaching efficiencies above 20%. However, its hygroscopic nature has prompted the quest for water-resistant alternatives. Recent studies have suggested that mixing MAPI with lower dimensional, bulky-A-site-cation perovskites helps mitigate this environmental instability. On the other hand, low dimensional perovskites suffer from poor device performance, which has been suggested to be due to limited out-of-plane charge carrier mobility resulting from structural dimensionality and large binding energy of the charge carriers. To understand the effects of dimensionality on performance, we systematically mixed MA-based 3D perovskites with larger A-site cations to produce dimethylammonium, iso-propylammonium, and t-butylammonium lead iodide perovskites. During the shift from MAPI to lower dimensional (LD) PSCs, the efficiency is significantly reduced by 2 orders of magnitude, with short-circuit current densities decreasing from above 20 mA cm-2 to less than 1 mA cm-2. In order to explain this decrease in performance, we studied the charge carrier mobilities of these materials using optical-pump/terahertz-probe, time-resolved microwave photoconductivity, and photoluminescence measurements. The results show that as we add more of the low dimensional perovskites, the mobility decreases, up to a factor of 20 when it reaches pure LD perovskites. In addition, the photoluminescence decay fitting is slightly slower for the mixed perovskites, suggesting some improvement in the recombination dynamics. These findings indicate that changes in structural dimensionality brought about by mixing A-site cations play an important role in determining the measured charge carrier mobility, and in the performance of perovskite solar cells., NSF (grant no. CBET-1605495), Institute for Soldier Nanotechnology (ISN) (grant no. W911NF-13-D-0001), NASA (grant no. NNX16AM70H)

Methylammonium lead iodide (MAPI) is a prototypical photo absorber in perovskite solar cells (PSCs), reaching efficiencies above 20%. However, its hygroscopic nature has prompted the quest to find water-resistant alternatives. Recent studies have suggested that mixing MAPI with lower dimensional, bulky-A-site-cation perovskites helps mitigate this environmental instability. On the other hand, low dimensional perovskites suffer from poor device performance, which has been suggested to be due to limited out-of-plane charge carrier mobility resulting from structural dimensionality and large binding energy of the charge carriers. To understand the effects of dimensionality on performance, we systematically mixed MA-based 3D perovskites with larger A-site cation, dimethylammonium, iso-propylammonium, and t-butylammonium lead iodide perovskites. During the shift from MAPI to lower dimensional (LD) PSCs, the efficiency is significantly reduced by 2 orders of magnitude, with short-circuit currents decreasing from above 20 mA/cm2 to less than 1 mA/cm2. In order to explain these decrease in performance, we studied the charge carrier mobilities of these materials using optical-pump/ terahertz-probe, time-resolved microwave photoconductivity, and photoluminescence measurements. The results show that as we add more of the low dimensional perovskites, the mobility decreases by a factor of 20 when it reaches pure LD perovskites. In addition, the photoluminescence decay fitting is slightly slower for the mixed perovskites, suggesting some improvement in the recombination dynamics. These findings indicate that changes in structural dimensionality by mixing A-site cations play an important role in measured charge carrier mobility, and in the performance of perovskite solar cells.

The mechanism and elemental composition that form the basis for the improved optical and electronic properties of perovskite solar cells are still not well understood. Here, we use synchrotron-based X-ray fluorescence to map the elemental distribution of ions in perovskite thin films with three different film thicknesses. To create a link to the electronic properties, we perform time-resolved photoluminescence spectroscopy and Kelvin probe force microscopy. We find that the elemental composition of the thin films is highly dependent on their thickness which resulted in different photoluminescence behaviors. In particular, we find that the I/Pb ratio is altered for single grains revealing the difference in grain composition. Our approach sheds light onto the fundamental properties occurring at the microscale which help to further engineer perovskite thin films and interfaces.

Characterization, material parameter extraction and subsequent optimization of solar cell devices is a highly time‑consuming and complex procedure. In this work, we propose a method for quick extraction of limiting material parameters in solar cell devices using a surrogate, physics-embedded, neural network model. This surrogate model, implemented by an autoencoder architecture trained with a physical numerical model, allows to quickly extract the device parameters of interest at a certain process condition by using only a small number of illumination dependent current-voltage (JV) measurements. Our surrogate model adequately links material parameters at a certain process condition to final device efficiency. The model provides physical insights about the location of the best performing and robust processing conditions in solar cell devices. We test our approach with GaAs and CH3NH3PbI3 (MAPbI) perovskite solar cells. The model allows to find a set of processing conditions that maximize the performance of both GaAs and MAPbI solar cells, and analogous processing conditions that minimize solar cell variability.

Although methylammonium lead iodide (MAPI) perovskite solar cells have reached efficiencies above 20%, the material is environmentally unstable. Mixing MAPI with lower dimensional (LD) perovskites has been suggested to improve its stability in recent studies. However, the LD-mixed perovskites have lower device performance, likely as a result of limited charge-carrier mobility due to their decreased structural dimensionality. To understand this effect, we mixed large-A-site cation LD perovskites, tert-butylammonium lead iodide, with MAPI, and performed a device performance diagnostics. The results suggested although the charge-carrier lifetime was improved, the mobility decreased by a factor of 20. This contributed to a reduction in device efficiency by 2 orders of magnitude, indicating that mobility plays an important role in 3D/LD perovskite mixtures.

Psittacine beak and feather disease virus (PBFDV) has been reported in many countries, such as Australia, Poland, the United States, South Africa, etc. In this study, the complete genome of a PBFDV isolate was determined and characterized from budgerigars in China.