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Abstract The ability to lubricate and resist wear at temperatures above 600 °C in an oxidative environment remains a significant challenge for metals due to their high-temperature softening, oxidation, and rapid degradation of traditional solid lubricants. Herein, we demonstrate that high-temperature lubricity can be achieved with coefficients of friction (COF) as low as 0.10-0.32 at 600-900 °C by tailoring surface oxidation in additively-manufactured Inconel superalloy. By integrating high-temperature tribological testing, advanced materials characterization, and computations, we show that the formation of spinel-based oxide layers on superalloy promotes sustained self-lubrication due to their lower shear strength and more negative formation and cohesive energy compared to other surface oxides. A reversible phase transformation between the cubic and tetragonal/monoclinic spinel was driven by stress and temperature during high temperature wear. To span Ni- and Cr-based ternary oxide compositional spaces for which little high-temperature COF data exist, we develop a computational design method to predict the lubricity of oxides, incorporating thermodynamics and density functional theory computations. Our finding demonstrates that spinel oxide can exhibit low COF values at temperatures much higher than conventional solid lubricants with 2D layered or Magnéli structures, suggesting a promising design strategy for self-lubricating high-temperature alloys.

Abstract Internal combustion engine fueled by carbon-free hydrogen (H2-ICE) offers a promising alternative for sustainable transportation. Herein, we report a facile and universal strategy through the physical mixing of Pt catalyst with zeolites to significantly improve the catalytic performance in the selective catalytic reduction of nitrogen oxides (NO x ) with H2 (H2-SCR), a process aiming at NO x removal from H2-ICE. Via the physical mixing of Pt/TiO2 with Y zeolite (Pt/TiO2 + Y), a remarkable enhancement of NO x reduction activity and N2 selectivity was simultaneously achieved. The incorporation of Y zeolite effectively captured the in-situ generated water, fostering a water-rich environment surrounding the Pt active sites. This environment weakened the NO adsorption while concurrently promoting the H2 activation, leading to the strikingly elevated H2-SCR activity and N2 selectivity on Pt/TiO2 + Y catalyst. This study provides a unique, easy and sustainable physical mixing approach to achieve proficient heterogeneous catalysis for environmental applications.

Abstract As a subfield of artificial intelligence (AI), machine learning (ML) has emerged as a versatile tool in accelerating catalytic materials discovery because of its ability to find complex patterns in high‐dimensional data. While the intricacy of cutting‐edge ML models, such as deep learning, makes them powerful, it also renders decision‐making processes challenging to explain. Recent advances in explainable AI technologies, which aim to make the inner workings of ML models understandable to humans, have considerably increased our capacity to gain insights from data. In this study, taking the oxygen reduction reaction (ORR) on {111}‐oriented Pt monolayer core–shell catalysts as an example, we show how the recently developed theory‐infused neural network (TinNet) algorithm enables a rapid search for optimal site motifs with the chemisorption energy of hydroxyl (OH) as a single descriptor, revealing the underlying physical factors that govern the variations in site reactivity. By exploring a broad design space of Pt monolayer core–shell alloys (∼17,000 candidates) that were generated from ∼1500 thermodynamically stable bulk structures in existing material databases, we identified novel alloy systems along with previously known catalysts in the goldilocks zone of reactivity properties. SHAP (SHapley Additive exPlanations) analysis reveals the important role of adsorbate resonance energies that originate from sp‐band interactions in chemical bonding at metal surfaces. Extracting physical insights into surface reactivity with explainable AI opens up new design pathways for optimizing catalytic performance beyond active sites.

Abstract Effective inhibition of intestinal lipid uptake is an efficient strategy for the treatment of disorders related to lipid metabolism. Sterol O‐acyltransferase 2 (SOAT2) is responsible for the esterification of free cholesterol and fatty acids into cholesteryl esters. We found that intestine‐specific SOAT2 knockout (Soat2I‐KO) mice was capable to prevent the development of dietary induced obesity due to reduced intestinal lipid absorption. Soat2 siRNA/CS‐PLGA nanoparticle system was constructed to enable intestinal delivery and inhibition of Soat2. This nanoparticle system was composed of PLGA‐block‐PEG and chitosan specifically delivering Soat2 siRNAs into small intestines in mice, effectively inhibit intestinal lipid uptake and resolving obesity. In revealing the underlying mechanism by which intestinal SOAT2 regulating fatty acid uptake, enhanced CD36 ubiquitination degradation was found in enterocytes upon SOAT2 inhibition. Insufficient free cholesterol esterification promoted endoplasmic reticulum stress and recruitment of E3 ligase RNF5 to activate CD36 ubiquitination in SOAT2 knockdown enterocytes. This work demonstrates a potential modulatory function of intestinal SOAT2 on lipid uptake highlighting the therapeutic effect on obesity by targeting intestinal SOAT2, exhibiting promising translational relevance in the siRNA therapeutic–based treatment for obesity.

Abstract Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard/

Currently, heterosis is an effective method for achieving high crop quality and yield worldwide. Owing to the challenges of breeding and the high cost of the F1 generation, the F2 generation is considered the more desirable hybrid offspring for agricultural production. The use of OJIP fluorescence provides rapid insights into various photosynthetic mechanisms. However, OJIP fluorescence has not been previously studied as an indicator of the rate of heterosis. Consequently, we investigated the relationship between photosynthetic characteristics and growth and developmental parameters in hybrid cotton cultivars. The findings showed a gradual decline in the photosynthetic performance of hybrid cotton as the number of generations increased. In comparison to the F3 generation, both the F1 and F2 generations showed minimal variations in parameters, thus maintaining hybrid dominant and emphasizing the agricultural production potential of the F2 generation. The JIP-test revealed significant differences in the relationship between ψEo and ϕEo parameters, as well as variations in the connections between the photo-response center and electron transfer efficiency, and between cotton yield and fiber quality in the hybrid progeny. These variations can serve as indicators for predicting the extent of hybrid dominance in cotton. The results indicated significant differences in the light and dark responses of the hybrid offspring. By using parents with similar photosynthetic performance as genetic resources for crossbreeding, the photosynthetic capacity of the hybrid progeny can be enhanced to facilitate the efficient absorption and conversion of light energy in crops.

Abstract Stereoselective ring-opening polymerization catalysts are used to produce degradable stereoregular poly(lactic acids) with thermal and mechanical properties that are superior to those of atactic polymers. However, the process of discovering highly stereoselective catalysts is still largely empirical. We aim to develop an integrated computational and experimental framework for efficient, predictive catalyst selection and optimization. As a proof of principle, we have developed a Bayesian optimization workflow on a subset of literature results for stereoselective lactide ring-opening polymerization, and using the algorithm, we identify multiple new Al complexes that catalyze either isoselective or heteroselective polymerization. In addition, feature attribution analysis uncovers mechanistically meaningful ligand descriptors, such as percent buried volume (%Vbur) and the highest occupied molecular orbital energy (E HOMO), that can access quantitative and predictive models for catalyst development.

Electrochemical conversion of ammonia to nitrogen has important energy and environmental applications but is hindered by lack of efficient electrocatalysts. Here the authors use quantum chemistry and machine learning to gain insights into the reaction mechanism and accelerate the design of highly active Ir-free trimetallic catalysts.

Abstract From the global perspective, stroke refers to a highly common cause of disability and death. Ischemic stroke (IS), attributed to blood vessel blockage, preventing the flow of blood to brain, acts as the most common form of stroke. Thus far, thrombolytic therapy is the only clinical treatment for IS with the approval from the FDA. Moreover, the physiology barrier complicates therapeutically and diagnostically related intervention development of IS. Accordingly, developing efficient and powerful curative approaches for IS diagnosis and treatment is urgently required. The advent of nanotechnology has brought dawn and hope to better curative and imaging forms for the management of IS. This work reviews the recent advances and challenges correlated with the nano drug delivery system for IS therapy and diagnosis. The overview of the current knowledge of the important molecular pathological mechanisms in cerebral ischemia and how the drugs cross the blood brain barrier will also be briefly summarized. Graphical Abstract

Machine learning is a powerful tool for screening electrocatalytic materials. Here, the authors reported a seamless integration of machine-learned physical insights with the controlled synthesis of structurally ordered intermetallic nanocrystals and well-defined catalytic sites for efficient nitrate reduction to ammonia.

Machine learning faces challenges in catalyst design due to its black-box nature. Here, the authors develop a theory-infused neural network approach that integrates deep learning algorithms with the well-established d-band theory of chemisorption for reactivity prediction of transition-metal surfaces.

Developing a generalizable model to describe adsorption processes at metal surfaces can be extremely challenging due to complex phenomena involved. Here the authors introduce a Bayesian learning approach based on ab initio data and the d-band model to capture the essential physics of adsorbate–substrate interactions.

In some plants, sucrose: sucrose 1-fructosyltransferase (1-SST) is the first irreversible key enzyme in fructan biosynthesis. Studies have shown that fructan accumulation enhances abiotic stress tolerance of plants. To investigate the role of 1-SST in drought stress responses, a total of 37 cotton plants expressing a 1-SST gene from Allium cepa were developed by Agrobacterium-mediated transformation. Under drought stress in the field, compared with wild-type, ectopic expression of Ac1-SST in cotton resulted in significantly higher soluble sugars (especially 1-kestose), proline and relative water contents, as well as decreased malondialdehyde content, which contributed to maintaining intracellular osmoregulation and reducing membrane damage. In addition, ectopic expression of Ac1-SST in cotton significantly improved the photosynthesis rate, performance of PSII (including Pn, Fv/Fm, WUE, ΦPSII, and PItotal) and plant growth under drought stress. Furthermore, compared with the wild-type, under the droughted field, the yield loss per square meter of transgenic cotton was reduced by an average of 20.9% over two consecutive years. Our results indicate that the Ac1-SST gene can be used to improve drought tolerance and yield of cotton varieties, and might also be a promising drought-resistant gene for improving other crop varieties.

The serious therapeutic obstacles to glioma treatment include poor penetration across the blood-brain barrier (BBB) and low accumulation of therapeutic drugs at tumor sites. In this study, borneol combined with CGKRK peptide (a ligand of the heparan sulfate which overexpress on the glioma cells) modified paclitaxel prodrug self-assembled redox-responsive nanoparticles (CGKRK-PSNPs) were hypothesized to enhance the BBB penetration ability and active tumor targeting efficiency, respectively. The resulting CGKRK-PSNPs possessed a spherical shape with a small particle size (105.61 ± 1.53 nm) and high drug loading for PTX (54.18 ± 1.13%). The drug release behavior proved that CGKRK-PSNPs were highly sensitive to glutathione (GSH) redox environment. The in vitro cell experiments suggested that CGKRK-PSNPs significantly increased the cellular uptake and cytotoxicity of U87MG cells, meanwhile CGKRK-PSNPs showed the low cytotoxicity against BCEC cells. Combined with borneol, CGKRK-PSNPs exhibited enhanced transportation across in vitro BBB model. In intracranial U87MG glioma-bearing nude mice, the higher accumulation of CGKRK-PSNPs combined with borneol was observed through real-time fluorescence image. Moreover, the in vivo anti-glioma results confirmed that CGKRK-PSNPs combined with borneol could improve the anti-glioma efficacy with the prolonged medium survival time (39 days). In conclusion, the collaborative strategy of CGKRK-PSNPs combined with borneol provided a promising drug delivery routine for glioblastoma therapy.

The aim of the experiment was to study the effects of feeding blends of sorbic acid, fumaric acid, and thymol (EOA) on growth performance, digestive functions, and immunity of broiler chickens. A total of 640 one-day-old male Cobb 500 chicks with similar BW (41.8 ± 0.6 g) were randomly divided into 4 dietary treatment groups consisting of 10 replicates with 16 birds per replicate and fed a basal diet until d 42 (CON) or diets with 0.15 g/kg enramycin during the grower period (AG), 0.30 g/kg EOA during the grower period (EG), or 0.30 g/kg EOA during the finisher period (EF). At d 42, the feed conversion ratio was reduced (P

The performance of supported metal nanoparticle catalysts can be tailored by metal-support interactions, but their use in catalyst design is still challenging. Here, the authors develop two-dimensional transition metal carbides as platforms for designing intermetallic compound catalysts that are efficient for light alkane dehydrogenations.

The Haber-Bosch process, producing NH3 from N2, is a crucial yet energetically demanding reaction, inspiring interest in the exploration of ambient-condition alternatives. Here, authors develop a palladium electrocatalyst that shows a high selectivity and activity for N2 reduction to NH3.

Density functional theory calculations were used to unravel the mechanism of CO2 electroreduction on SnOx surfaces. Under highly reducing conditions (< â0.6Â V vs. RHE), the SnO(101) surface with oxygen vacancies is likely the active phase for CO2 reduction. We showed that the proton-electron transfer to adsorbed *CO2 forming *OCHO, a key intermediate for producing HCOOH, is energetically more favorable than the formation of *COOH, justifying the selectivity trends observed on Sn-based electrocatalysts. With linear scaling relations, we propose the free formation energy of *CO2 at the oxygen vacancy as the reactivity descriptor. By engineering the strain of the SnO(101) surface, the selectivity towards HCOOH can be further optimized at reduced overpotentials. Keywords: CO2 electroreduction, Tin oxides, Strain effect, Scaling relations, Density functional theory

Drug delivery systems based on nanoparticles (nano-DDS) have aroused attentions for the treatment of glioblastoma (GBM), the most malignant brain cancer with a dismal prognosis. However, there are still numerous unmet challenges for traditional nano-DDS, such as the poor nanoparticle penetration, short retention in the GBM parenchyma and low glioma targeting ability. Herein, we used Pep-1 and CREKA peptides to construct a novel multifunctional GBM targeting nano-DDS (PC-NP). Pep-1 was used to overcome the blood–brain tumor barrier (BBTB) and home to glioma cells via interleukin-13 receptor-α2-mediated endocytosis, and CREKA was used to bind to fibrin–fibronectin complexes abundantly expressed in tumor microenvironment for enhanced retention in the GBM. Biological studies showed that the cellular uptake of PC-NP by U87MG cells was significantly enhanced compared with the non-targeting NP. Furthermore, CREKA modification increased the binding capacity of PC-NP to fibrin–fibronectin complexes as confirmed by the competition experiment. In accordance with the increased cellular uptake, PC-NP remarkably increased the cytotoxicity of its payload paclitaxel (PTX) against U87MG cells with an IC50 of 0.176 μg/mL. In vivo fluorescence imaging and antiglioma efficacy evaluation further confirmed that PC-NP accumulated effectively and penetrated deeply into GBM tissue. PC-NP-PTX exhibited a median survival time as long as 61 days in intracranial GBM-bearing mice. In conclusion, our findings indicated PC-NP as a promising nano-DDS for GBM targeting delivery of anticancer drugs.

Superparamagnetic iron-oxide nanoparticle (SPION) has gained tremendous attention for drug delivery applications due to their unique properties. In this study, we developed a dual targeted delivery system with paclitaxel (PTX) and SPION co-loaded PLGA nanoparticles, modified with Pep-1 peptide (Pep-NP-SPION/PTX), to achieve magnetic targeting and active targeting for tumor treatment. SPION was synthesized by a co-precipitation method and was then encapsulated with PTX simultaneously into PLGA nanoparticles. After that, the non-complex was conjugated with Pep-1 through chemical modification. The resulting Pep-NP-SPION/PTX showed a spherical morphology and an average size of 100 nm. The enhancement cellular uptake of Pep-NP-SPION was demonstrated in in vitro through cell experiments. The IC50 value of Pep-NP-SPION/PTX and NP-SPION/PTX was determined to be 10.2 and 19.4 μg/mL, respectively. A biodistribution study showed that obvious higher accumulations of Pep-NP-SPION was observed in tumors, compared with that of non-targeting nanocomposites. Moreover, under the condition of a magnetic field, both NP-SPION and Pep-NP-SPION exhibited much higher tumor distribution. Furthermore, Pep-NP-SPION/PTX presented desirable in vivo anti-tumor effects based on active targeting and magnetic targeting characteristics. Altogether, Pep-NP-SPION/PTX can offer magnetic targeting and receptor mediated targeting to enhance the anti-tumor outcome.