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This review discussed the dilemma of small data faced by materials machine learning. First, we analyzed the limitations brought by small data. Then, the workflow of materials machine learning has been introduced. Next, the methods of dealing with small data were introduced, including data extraction from publications, materials database construction, high-throughput computations and experiments from the data source level; modeling algorithms for small data and imbalanced learning from the algorithm level; active learning and transfer learning from the machine learning strategy level. Finally, the future directions for small data machine learning in materials science were proposed.

Electrochemical nitrate reduction to ammonia is a promising alternative strategy to the traditional Haber-Bosch process but suffers from a low Faradaic efficiency and limited ammonia yield due to the sluggish multi-electron/proton-involved steps. Herein, we report a typical hollow cobalt phosphide nanosphere electrocatalyst assembled on a self-supported carbon nanosheet array synthesized with a confinement strategy that exhibits an extremely high ammonia yield rate of 8.47 mmol h −1 cm −2 through nitrate reduction reaction, which is highly superior to previously reported values to our knowledge. In situ experiments and theoretical investigations reveal that the dynamic equilibrium between the generation of active hydrogen on cobalt phosphide and its timely consumption by nitrogen intermediates leads to a superior ammonia yield with a high Faradaic efficiency. This unique insight based on active hydrogen equilibrium provides new opportunities for large-scale ammonia production through electrochemical techniques and can be further used for carbon dioxide capture. While electrochemical conversion of nitrate to ammonia offers a renewable means to remediate waste compounds, it is challenging to achieve selective catalysis. Here, authors demonstrate a strategy to improve electrocatalytic ammonia production using cobalt phosphide on carbon nanosheet arrays.

The development of materials is one of the driving forces to accelerate modern scientific progress and technological innovation. Machine learning (ML) technology is rapidly developed in many fields and opening blueprints for the discovery and rational design of materials. In this review, we retrospected the latest applications of ML in assisting perovskites discovery. First, the development tendency of ML in perovskite materials publications in recent years was organized and analyzed. Second, the workflow of ML in perovskites discovery was introduced. Then the applications of ML in various properties of inorganic perovskites, hybrid organic–inorganic perovskites and double perovskites were briefly reviewed. In the end, we put forward suggestions on the future development prospects of ML in the field of perovskite materials.

In the field of surface defect detection, the scale difference of product surface defects is often huge. The existing defect detection methods based on Convolutional Neural Networks (CNNs) are more inclined to express macro and abstract features, and the ability to express local and small defects is insufficient, resulting in an imbalance of feature expression capabilities. In this paper, a Multi-Scale Feature Learning Network (MSF-Net) based on Dual Module Feature (DMF) extractor is proposed. DMF extractor is mainly composed of optimized Concatenated Rectified Linear Units (CReLUs) and optimized Inception feature extraction modules, which increases the diversity of feature receptive fields while reducing the amount of calculation; the feature maps of the middle layer with different sizes of receptive fields are merged to increase the richness of the receptive fields of the last layer of feature maps; the residual shortcut connections, batch normalization layer and average pooling layer are used to replace the fully connected layer to improve training efficiency, and make the multi-scale feature learning ability more balanced at the same time. Two representative multi-scale defect data sets are used for experiments, and the experimental results verify the advancement and effectiveness of the proposed MSF-Net in the detection of surface defects with multi-scale features.

The application of graphene for electrochemical energy storage has received tremendous attention; however, challenges remain in synthesis and other aspects. Here we report the synthesis of high-quality, nitrogen-doped, mesoporous graphene particles through chemical vapor deposition with magnesium-oxide particles as the catalyst and template. Such particles possess excellent structural and electrochemical stability, electronic and ionic conductivity, enabling their use as high-performance anodes with high reversible capacity, outstanding rate performance (e.g., 1,138 mA h g −1 at 0.2 C or 440 mA h g −1 at 60 C with a mass loading of 1 mg cm −2 ), and excellent cycling stability (e.g., >99% capacity retention for 500 cycles at 2 C with a mass loading of 1 mg cm −2 ). Interestingly, thick electrodes could be fabricated with high areal capacity and current density (e.g., 6.1 mA h cm −2 at 0.9 mA cm −2 ), providing an intriguing class of materials for lithium-ion batteries with high energy and power performance. Here, Lu and co-workers show the synthesis of high-quality, nitrogen-doped, mesoporous graphene particles using CVD with MgO as the catalyst and template. When used as the anode for a lithium ion battery, their unique architecture allows for excellent rate performance and cycling stability.

Oxaliplatin (L-OHP) is a well-known third-generation platinum anticancer drug with severe systemic- and neuro-toxicity. The main objective of the current research was to develop a targeted long-circulating thermosensitive smart-release liposome (LCTL) system for better therapeutic efficacy and less toxicity. The reverse-phase evaporation method (REV) was used to prepare L-OHP loaded LCTL (L-OHP/LCTL). The physical characteristics were evaluated including encapsulation efficiency (EE), size, zeta potential and stability. The release behavior, cytotoxicity and in vivo evaluation were also carried out. EE of LCTL was around 25% with a uniform size distribution, and LCTL achieved almost complete release at 42°C while it was only 10% at 37°C. Moreover, the LCTL showed significantly higher cytotoxicity at 42°C than that at 37°C. The in vivo results indicated LCTL could target tumors and enhance retention for more than 24 h, thereby enhancing anti-tumor efficacy on 4T1-bearing mice. These results indicated that LCTL not only possessed a prolonged circulation time but it also enhanced accumulation and achieved selective release at the tumor sites. Conclusively, LCTL could serve as a promising carrier for oxaliplatin delivery to treat solid tumors.

Global warming has intensified the intensity of compound drought‐hot extremes (CDHEs), posing more severe impacts on human societies and ecosystems than individual extremes. The vulnerability of global terrestrial ecosystems under CDHEs, along with its key influencing factors, remains poorly understood. Based on multiple remote sensing data, we construct a Vine Copula model to appraise vegetation vulnerability under CDHEs, and attribute it to climatic and biotic factors for five different vegetation types. High vulnerability is detected in central and southern regions of North America, eastern and southern regions of South America, Southern Africa, northern and western Europe, and northern and eastern Australia. The drier the climate, the higher will be the vulnerability. Furthermore, biodiversity and biomass are key biotic factors influencing the vulnerability of various vegetation types, such that ecosystems with richer biodiversity and higher biomass have lower vulnerability to CDHEs. The findings deepen understanding of terrestrial ecosystem response to CDHEs. Plain Language Summary Drought and hot‐related extremes often coincide or follow one another, known as compound drought‐hot extremes (CDHEs), adversely affecting various vegetation processes. Thus, investigating the response relationship between vegetation dynamics and CDHEs is critical for maintaining the sustainable development of ecosystems under the background of climate warming. High ecosystem vulnerability is detected in central and southern regions of North America, eastern and southern regions of South America, Southern Africa, northern and western Europe, and northern and eastern Australia. Climatic factors (precipitation, arid index, temperature, and radiation) dominate the vulnerability of all vegetation types. The drier the climate, the higher will be the vulnerability of vegetation to CDHEs. Furthermore, biodiversity and biomass are key biotic factors influencing the vulnerability. The results provide us with essential insights for managing and adapting terrestrial ecosystems against climate change and are of interest to a wide range of audiences, including but not limited to ecologists, hydrologists, and climatologists. Key Points A framework is developed to estimate the vegetation vulnerability in response to compound drought‐hot extremes and attribute it to various factors Vegetation vulnerability is higher for grasses and shrubs than for evergreen and deciduous forests Vegetation vulnerability is both affected by climate and biotic factors, but climate factors are more dominant

[Objective] Using multi-omics research methods to explore cytolytic activity-related genes through the immunoregulatory factors HAVCR2 (TIM3) affecting the survival and prognosis of lung adenocarcinoma. [Methods] We combined Cox single factor regression and lasso regression feature selection algorithm to screen out the key genes of cytolytic activity in lung adenocarcinoma, and applied multi-omics research to explore the clinical predictive value of the model, including onset risk, independent prognosis, clinical relevance, signal transduction pathways, drug sensitivity, and the correlation of immune regulatory factors, etc. TCGA data are used as the experimental group, and GEO data is used as the external data control group to verify the stability of the model. The survival curve was generated by the Kaplan–Meier method and compared by log-rank, and the Cox proportional hazard model was used for multivariate analysis. In this study, 10 fresh tissue samples of lung adenocarcinoma were collected for cellular immunohistochemical experiments to analyze the expression of immunoregulatory factors in cancer tissues, and the key immunoregulatory factors were verified and screened out. [Results] A total of 450 genes related to cytolytic activity were differentially expressed, of which 273 genes were up-regulated and 177 genes were down-regulated. A total of 91 key genes related to cytolytic activity related to the prognosis of lung adenocarcinoma were screened through Cox single factor regression. The ROC curve results showed that the AUC values of 1, 3, and 5 years in the training set and test set were all greater than 0.7, indicating that the model has a valid verification. The level of risk score is significantly related to the sensitivity of patients to AKT inhibitor VIII, Lenalidomide, and Tipifarnib. In addition, our study also found that receptor and MHC genes related to immunomodulatory, and chemokines, including HAVCR2, are more highly expressed in the low-risk group. [Conclusions] HAVCR2 (TIM3) immunoregulatory factors affect the expression of key genes that affect cytolytic activity in lung adenocarcinoma cells, and to some extent indirectly affect the survival and prognosis of patients with lung adenocarcinoma.

Resonant micro/nanoelectromechanical systems (MEMS/NEMS) with on-chip integrated excitation and readout components, exhibit exquisite gravimetric sensitivities which have greatly advanced the bio/chemical sensor technologies in the past two decades. This paper reviews the development of integrated MEMS/NEMS resonators for bio/chemical sensing applications mainly in air and liquid. Different vibrational modes (bending, torsional, in-plane, and extensional modes) have been exploited to enhance the quality (Q) factors and mass sensing performance in viscous media. Such resonant mass sensors have shown great potential in detecting many kinds of trace analytes in gas and liquid phases, such as chemical vapors, volatile organic compounds, pollutant gases, bacteria, biomarkers, and DNA. The integrated MEMS/NEMS mass sensors will continuously push the detection limit of trace bio/chemical molecules and bring a better understanding of gas/nanomaterial interaction and molecular binding mechanisms.

Proton exchange membrane fuel cells have been regarded as the most promising candidate for fuel cell vehicles and tools. Their broader adaption, however, has been impeded by cost and lifetime. By integrating a thin layer of tungsten oxide within the anode, which serves as a rapid-response hydrogen reservoir, oxygen scavenger, sensor for power demand, and regulator for hydrogen-disassociation reaction, we herein report proton exchange membrane fuel cells with significantly enhanced power performance for transient operation and low humidified conditions, as well as improved durability against adverse operating conditions. Meanwhile, the enhanced power performance minimizes the use of auxiliary energy-storage systems and reduces costs. Scale fabrication of such devices can be readily achieved based on the current fabrication techniques with negligible extra expense. This work provides proton exchange membrane fuel cells with enhanced power performance, improved durability, prolonged lifetime, and reduced cost for automotive and other applications. Proton exchange membrane fuel cells often suffer from low lifetimes and high cost. Here, the authors enhance the transient power performance and durability of these fuel cells by integrating a thin layer of tungsten oxide within the anode, which acts as a hydrogen reservoir, oxygen scavenger, and a regulator for the hydrogen-disassociation reaction.