Search

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.

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.

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

Realizing general inverse design could greatly accelerate the discovery of new materials with user-defined properties. However, state-of-the-art generative models tend to be limited to a specific composition or crystal structure. Herein, we present a framework capable of general inverse design (not limited to a given set of elements or crystal structures), featuring a generalized invertible representation that encodes crystals in both real and reciprocal space, and a property-structured latent space from a variational autoencoder (VAE). In three design cases, the framework generates 142 new crystals with user-defined formation energies, bandgap, thermoelectric (TE) power factor, and combinations thereof. These generated crystals, absent in the training database, are validated by first-principles calculations. The success rates (number of first-principles-validated target-satisfying crystals/number of designed crystals) ranges between 7.1% and 38.9%. These results represent a significant step toward property-driven general inverse design using generative models, although practical challenges remain when coupled with experimental synthesis. Agency for Science, Technology and Research (A*STAR) National Research Foundation (NRF) This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) program through the Singapore Massachusetts Institute of Technology (MIT) Alliance for Research and Technology’s Low Energy Electronic Systems (LEES) research program. F.O., S.S., and Q. Liang acknowledge support from Total Energies Research grant funded through MITei. Y.J. acknowledges the support from the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (No. 2021-0-02068, Artificial Intelligence Innovation Hub). J.L. and X.W. acknowledge support from the Ministry of Education Academic Research Fund R-279-000-532-114,. Y.L. is supported by the National Key Research and Development Program of China (Grant Nos. 2017YFB0702901 and 2017YFB0701502) and the National Natural Science Foundation of China (Grant No. 91641128). S.J. and K.H. acknowledge funding from the Accelerated Materials Development for Manufacturing Program at A*STAR via the AME Programmatic Fund by the Agency for Science, Technology and Research under Grant No. A1898b0043. G.X. is grateful for the support by the Scientific Computing and Data Analysis section of the Research Support Division at Okinawa Institute of Science and Technology Graduate University (OIST). Q.Li is supported by the National Research Foundation (NRF) fellowship grant NRFF13-2021-0106. A.G.A. acknowledges support from Solar Energy Research Institute of Singapore (SERIS). SERIS is a research institute at the National University of Singapore (NUS). SERIS is supported by the National University of Singapore (NUS), the National Research Foundation Singapore (NRF), the Energy Market Authority of Singapore (EMA), and the Singapore Economic Development Board (EDB).

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.

Pulmonary arterial hypertension is a progressive and fatal disease. Recent studies suggest that circular RNA (circRNAs/circs) can regulate various biological processes, including cell proliferation. Therefore, it is possible that circRNA may have important roles in pulmonary artery smooth muscle cell proliferation in hypoxic pulmonary hypertension (HPH). The aim of the present study was to determine the role and mechanism of circRNA‑glutamate metabotropic receptor 1 (circ‑Grm1; mmu_circ_0001907) in pulmonary artery smooth muscle cell (PASMC) proliferation and migration in HPH. High‑throughput transcriptome sequencing was used to screen circRNAs and targeted genes involved in HPH. Cell Counting Kit‑8 (CCK‑8), 5‑ethynyl‑2‑deoxyuridine and wound healing assays were employed to assess cell viability and migration. Reverse transcription‑quantitative PCR and western blotting were used to detect target gene expression in different groups. Bioinformatical approaches were used to predict the interaction probabilities of circ‑Grm1 and Grm1 with FUS RNA binding protein (FUS). The interactions of circ‑Grm1, Grm1 and FUS were evaluated using RNA silencing and RNA immunoprecipitation assays. The results demonstrated that circ‑Grm1 was upregulated in hypoxic PASMCs. Further experiments revealed that the knockdown of circ‑Grm1 could suppress the proliferation and migration of hypoxic PASMCs. Transcriptome sequencing revealed that Grm1 could be the target gene of circ‑Grm1. It was found that circ‑Grm1 could competitively bind to FUS and consequently downregulate Grm1. Moreover, Grm1 could inhibit the function of circ‑Grm1 by promoting the proliferative and migratory abilities of hypoxic PASMCs. The results also demonstrated that circ‑Grm1 influenced the biological functions of PASMCs via the Rap1/ERK pathway by regulating Grm1. Overall, the current results suggested that circ‑Grm1 was associated with HPH and promoted the proliferation and migration of PASMCs via suppression of Grm1 expression through FUS.

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.

While machine learning (ML) in experimental research has demonstrated impressive predictive capabilities, inductive reasoning and knowledge extraction remain elusive tasks, in part because of the difficulty extracting fungible knowledge representations from experimental data. In this manuscript, we use ML to infer the underlying dynamical differential equation (DE) from experimental data of degrading organic-inorganic methylammonium lead iodide (MAPI) perovskite thin films under environmental stressors (elevated temperature, humidity, and light). We apply a sparse regression algorithm that automatically identifies the differential equation describing the dynamics from time-series data. We find that the underlying DE governing MAPI degradation across a broad temperature range of 35 to 85{\deg}C is described minimally with three terms (specifically, a second-order polynomial), and not a simple single-order reaction (i.e. 0th, 1st, or 2nd-order reaction). We demonstrate how computer-derived results can aid the researcher to develop profound mechanistic insights. This DE corresponds to the Verhulst logistic function, which describes reaction kinetics analogous in functional form to autocatalytic or self-propagating reactions, suggesting future strategies to suppress MAPI degradation. We examine the robustness of our conclusions to experimental luck-of-the-draw variance and Gaussian noise using a combination of experiment and simulation, and describe the experimental limits within which this methodology can be applied. Our study demonstrates the application of scientific ML in experimental chemical and materials systems, highlighting the promise and challenges associated with ML-aided scientific discovery.

Modern machine learning and autonomous experimentation schemes in materials science rely on accurate analysis of the data ingested by these models. Unfortunately, accurate analysis of the underlying data can be difficult, even for domain experts, complicating the training of the models intended to drive experiments. This is especially true when the goal is to identify the presence of weak signatures in diffraction or spectroscopic datasets. In this work, we examine a set of as-obtained diffraction data that track the phase transition from monoclinic to tetragonal in a Nb-doped VO2 film as a function of temperature and dopant concentration. We then task a set of domain experts and a set of machine learning experts with identifying which phase is present in each diffraction pattern manually and algorithmically, respectively; in both cases, the labels can vary dramatically, especially at the phase boundaries. We use the mode of the labels and the Shannon entropy as a method to capture, preserve and propagate consensus labels and their variance. Further we use the expert labels as a benchmark and demonstrate the use of Shannon entropy weighted scoring to test the performance of machine learning generated labels. Finally, we propose a material data challenge centered around generating improved labeling algorithms. This real-world dataset curated with expert labels can act as test bed for new algorithms. The raw data, annotations and code used in this study are all available online at data.gov and the interested reader is encouraged to replicate and improve the existing models

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 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...

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.

In the field of machine learning (ML) for materials optimization, active learning algorithms, such as Bayesian Optimization (BO), have been leveraged for guiding autonomous and high-throughput experimentation systems. However, very few studies have evaluated the efficiency of BO as a general optimization algorithm across a broad range of experimental materials science domains. In this work, we evaluate the performance of BO algorithms with a collection of surrogate model and acquisition function pairs across five diverse experimental materials systems, namely carbon nanotube polymer blends, silver nanoparticles, lead-halide perovskites, as well as additively manufactured polymer structures and shapes. By defining acceleration and enhancement metrics for general materials optimization objectives, we find that for surrogate model selection, Gaussian Process (GP) with anisotropic kernels (automatic relevance detection, ARD) and Random Forests (RF) have comparable performance and both outperform the commonly used GP without ARD. We discuss the implicit distributional assumptions of RF and GP, and the benefits of using GP with anisotropic kernels in detail. We provide practical insights for experimentalists on surrogate model selection of BO during materials optimization campaigns.

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.

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.

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 (MAPbI

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.

Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach for gallium arsenide (GaAs) solar cells that identifies the root cause(s) of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond traditional black-box optimization. Our Bayesian network approach links a key GaAs process variable (growth temperature) to material descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device performance parameters (e.g., cell efficiency). For this purpose, we combine a Bayesian inference framework with a neural network surrogate device-physics model that is 100× faster than numerical solvers. With the trained surrogate model and only a small number of experimental samples, our approach reduces significantly the time-consuming intervention and characterization required by the experimentalist. As a demonstration of our method, in only five metal organic chemical vapor depositions, we identify a superior growth temperature profile for the window, bulk, and back surface field layer of a GaAs solar cell, without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above traditional grid search methods.

Lead halide perovskites (LHPs) are promising semiconductors for optoelectronic applications. In LHP solar cells, the focus thus far has been mainly on compositional optimization of the MHP layer, without much understanding of the effects of compositional mixing on structure and texture. This is a serious gap in our knowledge because research has shown that texture underlies the mechanisms of charge carrier and ionic transport. Therefore, it is essential to understand the mechanisms that drive changes in texture in LHPs. This work examines the effect of moisture and composition on the structure and texture of LHPs and their impacts on optoelectronic properties. Exposure to moisture is shown to induce a crystallographic reorientation in the polycrystalline films, which is also dependent on the amount of organic cation material present at the surface. For films with an excess of organic halide, moisture was shown to induce texture in the (001) plane contributing to the enhancement of photocurrents and long-Term device stability. This work shows the importance of texture for the electronic properties of LHPs with a special emphasis on charge carrier extraction in optoelectronic devices.

In previous research, sucrose and citric acid were used to synthesize an eco-friendlyplywood adhesive. Herein, further research was performed to determine the optimal hot-pressingconditions and curing behavior of a sucrose-citric acid (SC) adhesive. The results of dry and wetshear strength measurements showed that the optimal hot-pressing temperature, hot-pressing time,and spread rate of plywood samples bonded by the SC adhesive were 190 &deg, C, 7 min, and 140 g/m2,respectively. When plywood was bonded at the optimal hot-pressing conditions, the wet shearstrength met the requirements of the China National Standard GB/T 9846-2015. Thermal analysisshowed that the thermal degradation and endothermic reaction temperatures of the SC 25/75adhesive were lower than either sucrose or citric acid individually. In addition, the insoluble massproportion increased with the heating temperature and time. The Pyrolysis Gas Chromatographyand Mass Spectrometr (Py-GC/MS) analysis confirmed that the SC adhesive was cured by thereaction between furan compounds, saccharide, and citric acid, and the resulting polymer appearedto be joined by ether linkages.