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Mineral phases in which vanadium (V) and heavy-rare-earth elements (HREEs) coexist are rarely documented. Here, we report a new V-HREE-bearing silicate mineral species, wenlanzhangite-(Y), which is a vanadiferous derivate of jingwenite-(Y) [Y2Al2V24+(SiO4)2O4(OH)4]$\begin{array}{} \displaystyle {\rm [Y_2Al_2V_2^{4+}(SiO_4)_2O_4(OH)_4]} \end{array}$coexisting with jingwenite-(Y) in bedded/massive ores at Yushui, South China. Wenlanzhangite-(Y) forms as a dark brown, 70–100 μm thick rim on a core domain of jingwenite-(Y), which occurs as 100–200 μm columnar crystals. The color, streak, luster, and hardness (Mohs) are dark brown, yellow-gray, vitreous, and ~4, respectively. Compared to jingwenite-(Y), wenlanzhangite-(Y) has higher vanadium and lower aluminum contents. Calculated on the basis of 8 cations, the empirical formula is (Y1.26Dy0.17Er0.11Gd0.09Yb0.09Nd0.09Sm0.06Sc0.04Ho0.03Ce0.02Tb0.02Tm0.02Pr0.01)Σ2.00(V1.463+Al0.54)Σ2.00V24+(SiO4)2O4(OH)4$\begin{array}{} \displaystyle {\rm (V^{3+}_{1.46}Al_{0.54})_{\Sigma2.00}V_2^{4+}(SiO_4)_2O_4(OH)_4} \end{array}$, Which can be simplified to the ideal formula Y2V23+V24+(SiO4)2O4(OH)4$\begin{array}{} \displaystyle {\rm Y_2V_2^{3+}V_2^{4+}(SiO_4)_2O_4(OH)_4} \end{array}$.

The magnetic alignment of molecules, which exploits the anisotropy of diamagnetic susceptibility, provides a clean and versatile approach to the structural manipulation of semiconducting polymers. Here, the magnetic-alignment dynamics of two molecular-weight (MW) batches of a diketopyrrolopyrrole (DPP)-based copolymer (PDVT-8) were investigated. Microstructural characterizations revealed that the magnetically aligned, high-MW(Mn= 53.7 kDa) PDVT-8 film exhibited a higher degree of backbone chain alignment and film crystallinity compared with the low-MW(Mn= 17.6 kDa) PDVT-8 film grown via the same magnetic alignment method. We found that as the MWincreases, the degree of preaggregation of the polymer molecules in solution significantly increases and the aggregation mode changes from H-aggregation to J-aggregation through a cooperative assembly mechanism. These events improved the responsiveness of high-MWpolymer molecules to magnetic fields. Field-effect transistors based on the magnetic aligned high-MWPDVT-8 films exhibited a 6.8-fold increase in hole mobility compared to the spin-coated films, along with a mobility anisotropy ratio of 12.6. This work establishes a significant correlation among chain aggregation behavior in solution, polymer film microstructures, magnetic responsiveness, and carrier transport performance in donor–acceptor polymer systems.

A new mineral species, yuchuanite-(Y), ideally Y2(CO3)3·H2O, has been discovered and characterized in the Yushui Cu deposit in South China. The mineral occurs in bedded/massive ore and is associated with bornite, chalcopyrite, galena, sphalerite, bastnäsite-(Y), xenotime-(Y), anhydrite, and quartz. Individual crystals range in size from 30 to 300 μm. No twinning is observed. The mineral is colorless and transparent with a vitreous luster. The calculated density is 3.62 g/cm3. An electron microprobe analysis yields the empirical formula (based on 10 O apfu), (Y1.61Yb0.11Er0.11Dy0.08Ho0.03Gd0.02Tm0.02)Σ1.99(CO3)3·H2O.

Catalyst screening is a critical step in the discovery and development of heterogeneous catalysts, which are vital for a wide range of chemical processes. In recent years, computational catalyst screening, primarily through density functional theory (DFT), has gained significant attention as a method for identifying promising catalysts. However, the computation of adsorption energies for all likely chemical intermediates present in complex surface chemistries is computationally intensive and costly due to the expensive nature of these calculations and the intrinsic idiosyncrasies of the methods or data sets used. This study introduces a novel machine learning (ML) method to learn adsorption energies from multiple DFT functionals by using invariant molecular representations (IMRs). To do this, we first extract molecular fingerprints for the reaction intermediates and later use a Siamese-neural-network-based training strategy to learn invariant molecular representations or the IMR across all available functionals. Our Siamese network-based representations demonstrate superior performance in predicting adsorption energies compared with other molecular representations. Notably, when considering mean absolute values of adsorption energies as 0.43 eV (PBE-D3), 0.46 eV (BEEF-vdW), 0.81 eV (RPBE), and 0.37 eV (scan+rVV10), our IMR method has achieved the lowest mean absolute errors (MAEs) of 0.18 0.10, 0.16, and 0.18 eV, respectively. These results emphasize the superior predictive capacity of our Siamese network-based representations. The empirical findings in this study illuminate the efficacy, robustness, and dependability of our proposed ML paradigm in predicting adsorption energies, specifically for propane dehydrogenation on a platinum catalyst surface.

Since the depth information of images facilitates the analysis of the spatial distance of objects in computer vision applications, it is necessary to protect the image depth information. Thus this article proposes a novel red-green-blue-depth (RGB-D) image protection algorithm, which is implemented with the finite-time synchronization (FTS) of neural networks (NNs) with proportional delays via the quantized intermittent control to derive the system synchronization criterion based on Lyapunov stability theory. The performance of RGB-D image protection depends on the synchronization error of the system by driving the system sequence to encrypt the RGB-D image and responding to the system sequence to decrypt the encrypted image. Subsequently, the validity of the proposed criteria is verified by simulation examples, and the practical application of RGB-D image protection is verified.

Biplanar deflectometry based on transparent screens (TSs) does not necessitate any movement of the screen or the addition of a spectroscope, mitigating the issues of complex calibration processes and reduced accuracy. This method usually only requires the calibration of the thickness $d$ and refractive index $\eta $ of the TS. However, traditional refraction parameter calibration methods require repeatedly moving the camera or standard flat mirror (Sfm), making the calibration process complex; to address this issue, the current article proposes a novel calibration method for the refractive parameters of TSs, which analyzes the optical path model of camera detection light refracting on TSs through inverse ray tracing. The mathematical model of the incident vector related to TS thickness $d$ and refractive index $\eta $ is also constructed. The refraction parameters of the TS can be obtained by inputting a known incident vector (constructed by a Sfm) into the model. This method does not necessitate repeated movement of the camera or the Sfm; the refraction parameters $d$ and $\eta $ of the TS can be calibrated in the calibration stage, greatly reducing the calibration complexity. Finally, a spherical mirror with a radius of curvature of 1000 mm is measured. The root-mean-square error (RMSE) is $3.56~\mu \text{m}$ when considering the effect of refraction, and $8.55~\mu \text{m}$ without considering the effect of refraction, which proves the effectiveness of the proposed method.

Symbiotic nitrogen fixation in legume nodules requires substantial energy investment from host plants, and soybean (Glycine max(L.) supernodulation mutants show stunting and yield penalties due to overconsumption of carbon sources. We obtained soybean mutants differing in their nodulation ability, among which rhizobially induced cle1a/2a(ric1a/2a) has a moderate increase in nodule number, balanced carbon allocation, and enhanced carbon and nitrogen acquisition. In multi-year and multi-site field trials in China, two ric1a/2alines had improved grain yield, protein content and sustained oil content, demonstrating that gene editing towards optimal nodulation improves soybean yield and quality.

The development of high-efficiency oxygen evolution reaction (OER) electrocatalysts for the production and conversion of clean energy is paramount yet also full of challenges. Herein, we proposed a simple and universal method to precisely fabricate the hierarchically structured CuO/TMOs loaded on Cu foil (CuO/TMOs/CF) (TMO represents Mn3O4, NiO, CoO, and CuO) nanorod-array electrodes as a highly active and stable OER electrocatalyst, employing Cu(OH)2/CF as a self-sacrificing template by the subsequent H2O2-induced chemical deposition (HiCD) and pyrolysis process. Taking CuO/Mn3O4/CF as an example, we systematically investigated its structure–performance relationship via experimental and theoretical explorations. The enhanced OER activity can be ascribed to the rational design of the nanoarray with multiple synergistic effects of abundant active sites, excellent electronic conductivity of the metallic Cu foil substrate, strong interface charge transfer, and quasi-superhydrophilic/superaerophobic property. Consequently, the optimal CuO/Mn3O4/CF presents an overpotential of 293 mV to achieve a current density of 20 mA cm–2in 1.0 M KOH media, comparable to that of commercial RuO2(282 mV), delivering excellent durability by the electrolysis of water at a potential of around 1.60 V [vs reversible hydrogen electrode (RHE)] without evident degeneration. This work might offer a feasible scheme for developing a hybrid nanoarray OER electrocatalyst via regulating electron transportation and mass transfer.

High pressure CO overturns the C2+/H2Faradaic efficiency ratio in electroreduction of CO to C2+via facilitating the C–C coupling while suppressing the HER.

The unicellular green alga Chlamydomonas reinhardtii(hereafter Chlamydomonas) possesses both plant and animal attributes, and it is an ideal model organism for studying fundamental processes such as photosynthesis, sexual reproduction, and life cycle. N6-methyladenosine(m6A) is the most prevalent mRNA modification, and it plays important roles during sexual reproduction in animals and plants. However, the pattern and function of m6A modification during the sexual reproduction of Chlamydomonasremain unknown. Here, we performed transcriptome and methylated RNA immunoprecipitation sequencing (MeRIP-seq) analyses on six samples from different stages during sexual reproduction of the Chlamydomonaslife cycle.The results show that m6A modification frequently occurs at the main motif of DRAC (D = G/A/U, R = A/G) in ChlamydomonasmRNAs. Moreover, m6A peaks in ChlamydomonasmRNAs are mainly enriched in the 3′ untranslated regions (3′UTRs) and negatively correlated with the abundance of transcripts at each stage. In particular, there is a significant negative correlation between the expression levels and the m6A levels of genes involved in the microtubule-associated pathway, indicating that m6A modification influences the sexual reproduction and the life cycle of Chlamydomonasby regulating microtubule-based movement. In summary, our findings are the first to demonstrate the distribution and the functions of m6A modification in ChlamydomonasmRNAs and provide new evolutionary insights into m6A modification in the process of sexual reproduction in other plant organisms.

The unicellular green alga Chlamydomonas reinhardtii(hereafter Chlamydomonas) possesses both plant and animal attributes, and it is an ideal model organism for studying fundamental processes such as photosynthesis, sexual reproduction, and life cycle. N6-methyladenosine(m6A) is the most prevalent mRNA modification, and it plays important roles during sexual reproduction in animals and plants. However, the pattern and function of m6A modification during the sexual reproduction of Chlamydomonasremain unknown. Here, we performed transcriptome and methylated RNA immunoprecipitation sequencing (MeRIP-seq) analyses on six samples from different stages during sexual reproduction of the Chlamydomonaslife cycle.The results show that m6A modification frequently occurs at the main motif of DRAC (D = G/A/U, R = A/G) in ChlamydomonasmRNAs. Moreover, m6A peaks in ChlamydomonasmRNAs are mainly enriched in the 3′ untranslated regions (3′UTRs) and negatively correlated with the abundance of transcripts at each stage. In particular, there is a significant negative correlation between the expression levels and the m6A levels of genes involved in the microtubule-associated pathway, indicating that m6A modification influences the sexual reproduction and the life cycle of Chlamydomonasby regulating microtubule-based movement. In summary, our findings are the first to demonstrate the distribution and the functions of m6A modification in ChlamydomonasmRNAs and provide new evolutionary insights into m6A modification in the process of sexual reproduction in other plant organisms.

Copper-catalyzed dynamic kinetic resolution of secondary phosphine oxides has been successfully developed, providing a general method for the gram-scale enantioselective synthesis of P-stereogenic cyclic phosphine oxides with high yields and high enantioselectivities. The products could be easily reduced to the corresponding useful P(III)-stereogenic cyclic phosphines. A mechanism of the dynamic kinetic resolution involving the unusual rapid racemization of SPOs has been proposed.

Phase measuring deflectometry (PMD) is widely used to measure the surface of specular objects. The planar screen used in traditional PMD systems has the drawbacks of a small field of view of measurement and a large virtual image depth range. This problem can be effectively solved by using the curved screen. As the arc structure of the curved screen is different to the planar structure required in Zhang’s method, the existing methods mainly rely on the stereo vision of two cameras to obtain the screen position. This article presents a novel PMD based on a curved screen and single camera. A method of calibrating a curved screen by constructing a virtual planar screen is presented. The method only needs one camera to calibrate the curved screen directly with high accuracy. In addition, the change from planar screen to curved screen makes the traditional phase–gradient–height relation invalid. Based on the comprehensive analysis of space optical path models, a novel phase–gradient–height relation is presented. The relation is a general formula. It can be used for any curved screen, including the planar screen. Simulation and quantitative comparison experiments verify the accuracy and reliability of the proposed calibration method. The measurement results of concave mirror show that the proposed method has higher accuracy and a larger field of view in measuring specular objects than the traditional methods.

PsbS protein is essential for the rapid induction of non-photochemical quenching (NPQ) under low night temperatures (LNTs), but its stability is often affected by adverse environmental conditions. However, the regulatory mechanism for the stability of PsbS or chloroplast proteins remains to be fully characterized. We show that LNT decreases NPQ levels and SlPsbS protein abundance in tomato leaves. LNT-activated chloroplast vesicles (SlCVs) targeting the chloroplasts induce the formation of CV-containing vesicles (CCVs) containing SlPsbS, exported from the chloroplasts. Subsequently, SlCV and SlPsbS contact COP9 signalosome subunit 5A (SlCSN5A) in the cytosol and are ubiquitinated and degraded. Genetic evidence demonstrates that the overexpression of SlCV aggravates SlPsbS protein degradation, whereas silencing of SlCSN5 and SlCV delays LNT-induced NPQ reduction and SlPsbS protein turnover. This study reveals a ubiquitin-dependent degradation pathway of chloroplast proteins co-mediated by CV and CSN5A, thereby providing a basic reference for the regulation of chloroplast protein stability under stress conditions.

ABSTRACTAs the complexity of spacecraft function has increased, the requirements for assembly quality and reliability have become more stringent. The current digital assembly simulation stays in the idealized structure and method verification, however, the spacecraft assembly process relies on manual and discrete operations, making it challenging for real time assembly quality tracing and process clarification. This paper takes the spacecraft assembly process as the research object, studies the real-time data acquisition method that fuses the acquisition node and the assembly process, and establishes the virtual-real mapping process around the central data space. A Digital twin system corresponding to the actual spacecraft assembly process was constructed based on the spacecraft assembly cell unit. The states of the manufacturing process in the physical world are fed back to the virtual space. The problems faced during assembly are reflected in real-time, and the corresponding behaviour is simulated. Based on the real-time data acquisition, real-time monitoring and numerical analysis for comparison are realized, providing a feasible solution for the application of Digital twin in the spacecraft assembly scenario.

This manuscript summarizes observations made during the conversion of propanoic acid over Pt/SiO2under H2-rich environments. Under these conditions, Pt is active for hydrogenation and hydrodeoxygenation, which leads to the formation of propionaldehyde, 1-propanol, and propane. Pt also facilitates decarbonylation of propanoic acid and propionaldehyde, which forms ethane and CO. The accumulation of CO with increasing residence time poisons the Pt catalyst and makes it difficult to achieve high propanoic acid conversions on practical time scales. During the conversion of propanoic acid on Pt/SiO2, sequential reactions play a critical role in determining product distributions. We resolve their contributions through analysis of rates and selectivities during the conversion of propanoic acid, propionaldehyde, 1-propanol, CO, and CO2in various environments. We conclude that the main challenge facing the selective production of partial hydrodeoxygenation products─namely propionaldehyde and 1-propanol─is that one must facilitate dehydroxylation of propanoic acid while avoiding thermodynamically favorable decarbonylation and alcohol hydrogenolysis pathways. Because these side reactions are effectively irreversible under hydrodeoxygenation conditions, this can only be accomplished by suppressing rates of decarbonylation and 1-propanol hydrogenolysis. We reconcile macroscopic trends with a reaction mechanism that includes parallel and sequential reactions occurring during the hydrodeoxygenation of propanoic acid over Pt/SiO2.

Dynamic economic dispatch (DED) considering valve-point effects and transmission losses is a complex constrained optimization problem with non-smooth, non-linear and non-convex characteristics which has always been a significant challenge. To tackle the DED problem, an enhanced exploratory whale optimization algorithm (EEWOA) and a constraint handling technique considering the unit efficiency are proposed. Firstly, an adaptive enhanced exploratory prey mechanism is created to enhance population diversity of whale swarm, and the EEWOA method is developed. Secondly, a constraint handling technology is designed to improve the variable repairing ability, including coarse-tuning by heuristics and fine-tuning by forced repair technique. The proposed EEWOA is compared with several state-of-the-art optimization algorithms in terms of efficiency and effectiveness on 13 classical benchmarks, and three DED with different scales. According to the simulation results, EEWOA shows outstanding advantages on the benchmarks and the DED cases.

Notorious shuttle effect and sluggish redox kinetics as major bottlenecks have nowadays hindered the commercial implementation of lithium–sulfur batteries. The activity design of catalysts has attracted increasing attention in this realm thus far. Herein, we devise a Co-based electrocatalytic tandem (Co–N–P) encompassing (N,P)-coordinated Co single atoms and Co2P nanoparticles for guiding the dual-directional sulfur evolution reactions. Such a Co–N–P tandem synergizes high atom utilization, large catalyst loading, and smooth charge migration, thereby resulting in high activity for dictating the Li2S nucleation and decomposition. As a result, the full cell incorporating the Co–N–P-modified separator harvests 0.1 % capacity decay after 500 cycles at 1.0 C. In addition, a favorable areal capacity output of 4.2 mAh/cm2is obtained under a sulfur loading of 5.3 mg/cm2. We anticipate that this work would offer insight into the hybrid catalyst design affording high activity and stability for emerging energy applications.

Microkinetic models based on parameters obtained from density functional theory and transition state theory have been developed for the hydrodeoxygenation (HDO) of propanoic acid, a model lignocellulosic biomass-derived organic acid, over the flat Pd(100) and Pd(111) surfaces in both vapor and liquid phase reaction conditions. The more open Pd(100) surface was found to be 3–7 orders of magnitude more active than the Pd(111) surface in all reaction environments, indicating that the (111) surface is not catalytically active for the HDO of propanoic acid. Over Pd(100) and in vapor phase, liquid water, and liquid 1,4-dioxane, propanoic acid hydrodeoxygenation follows a decarbonylation (DCN) mechanism that is facilitated by initial α- and β-carbon dehydrogenation steps, prior to the rate controlling C–OH and (partially rate controlling) C–CO bond dissociations. Only over Pd(111) and aqueous reaction environments is the decarboxylation (DCX) preferred over the DCN with the C–CO2step being rate controlling.

The realization of efficient oxygen evolution reaction (OER) is critical to the development of multiple sustainable energy conversion and storage technologies, especially hydrogen production via water electrolysis. To achieve the massive application of hydrogen energy and mass-scale hydrogen production from water splitting drives the pursuit of competent precious-metal-free electrocatalysts in acidic media, where the hydrogen evolution reaction (HER) is more facilitated. However, the development of high-efficient and acid-stable OER electrocatalysts, which are robust to function stably at high oxidation potentials in the acidic electrolyte, remains a great challenge. This article contributes a focused, perceptive review of the up-to-date approaches toward this emerging research field. The OER reaction mechanism and fundamental requirements for oxygen evolution electrocatalysts in acid are introduced. Then the progress and new discoveries of precious-metal-free active materials and design concepts with regard to the improvement of the intrinsic OER activity are discussed. Finally, the existing scientific challenges and the outlooks for future research directions to the fabrication of emerging, earth-abundant OER electrocatalysts in acid are pointed out.

The effect of two solvents, liquid water and 1,4-dioxane, has been studied from first-principles on the hydrodeoxygenation of propionic acid over a Ni(111) catalyst surface model. A mean-field microkinetic model was developed to investigate these effects at a temperature of 473 K. Under all reaction conditions, a decarbonylation mechanism is favored significantly over a decarboxylation pathway. Although no significant solvent effects were observed on the decarbonylation rate, a substantial solvent stabilization of two key surface intermediates in the decarboxylation mechanism, CH3CCOO and CH3CHCOO, leads to a notable increase of the decarboxylation rate by 2 orders of magnitude in liquid water and by 1 order of magnitude in liquid 1,4-dioxane. Furthermore, a significant solvent stabilization of the transition state of C–H bond cleavage of the α-carbon of CH3CHCO, relative to the stabilization of the C–C bond cleavage of the α-carbon of CH3CHCO, leads to a change in dominant pathway in the liquid phase environments. Finally, a sensitivity analysis shows that the C–OH bond cleavage of propionic acid and C–C bond cleavage of the α-carbon of CH3CHCO are the most rate controlling states in the gas phase. In contrast, in solvents the dehydrogenation of CH3CHCO becomes the most influential step. This shift in rate controlling state is attributed to the solvent effect on the dehydrogenation of CH3CHCO, which is facilitated in the aqueous phase. Overall, it is likely that the investigated (111) facet of Ni is not active for the hydrodeoxygenation of propionic acid in either the gas or the liquid phase and other Ni facets or phases must be responsible for the experimentally observed kinetics.

Simultaneously improving device efficiency and stability is the most important issue in perovskite solar cell (PSC) research. Here, we strategically introduce a multi-functional interface layer (MFIL) with integrated roles of: (1) electron transport, (2) moisture barrier, (3) near-infrared photocurrent enhancement, (4) trap passivation, and (5) ion migration suppression to enhance the device performance. The narrow-band-gap non-fullerene acceptor, Y6, was screened out to replace the most commonly used PCBM in the inverted PSCs. A significantly improved power conversion efficiency of 21.0% was achieved, along with a remarkable stability (up to 1,700 h) without encapsulation under various external stimuli (light, heat, and moisture). Furthermore, systematic studies of the molecular orientation or passivation and the charge carrier dynamics at the interface between perovskite and MFIL were presented. These results offer deep insights for designing advanced interlayers and establish the correlations between molecular orientation, interface molecular bonding, trap state density, non-radiation recombination, and the device performance.

Perovskite solar cells (PSCs) toward practical application relies on high efficiency, long lifetime, low toxicity, and device up-scaling. To realize large-area PSCs, a green solution-bathing strategy is delivered to prepare high-performance PSCs. By utilizing 2-pentanol as a green solvent and formamidinium chloride (FACl) as a solute in the green solution-bathing process, perovskite films with enlarged grain sizes, improved crystallinity, and alleviated defect state density were obtained, resulting in the enhancement in the power conversion efficiency of PSCs. Coupled with 2-pentanol and FACl, both a champion efficiency of 21.03% for small cells (0.103 cm2) and an efficiency of over 18% for large size (1.00 cm2) were obtained based on the GSB process, which can outperform its counterpart made via the commonly used antisolvent-dropping method. In addition, a large perovskite film (5 cm × 5 cm) with obvious mirror effect was successfully prepared. Our innovative approach paves the way to promote device up-scaling of PSCs via an environmentally friendly technique.

Understanding the carbon-tolerant mechanisms from a microscopic view is of special importance to develop proper anodes for solid oxide fuel cells. In this work, we employed density-functional theory calculations to study the CH4reaction mechanism over a Ni/TiO2nanostructure, which experimentally demonstrated good carbon tolerance. Six potential pathways for methane reforming reactions were studied over the Ni/TiO2(110) surface under both dry and wet atmospheres, and the main concerns were focused on the impact of TiO2and Ni/TiO2interface on CO/H2 formation. Our calculations suggest that the reaction between carbon and the interfacial lattice oxygen to form CO* is the dominant pathway for CH4reforming under both dry and wet atmospheres, and intervention of steam directly to oxidize C* with its dissociated OH* group is less favorable in energy than that to wipe off oxygen vacancy to get ready for next C* oxidation. In all investigated paths, desorption of CO* is one of the most difficult steps. Fortunately, CO* desorption can be greatly promoted by the large heat released from the previous CO* formation process under wet atmosphere. H2O adsorption and dissociation over the TiO2surface are found to be much easier than those over Ni, yttria stabilized zirconia (YSZ) and CeO2, which should be the key reason for the greatly depressed carbon deposition over Ni-TiO2particles than traditional YSZ-Ni and CeO2-Ni anode. Our study presents the detailed CO* formation mechanism in CH4reforming process over the Ni/TiO2surface, which will benefit future research for exploring new carbon-tolerant solid oxide fuel cell anodes.

Computational catalyst discovery involves the development of microkinetic reactor models based on estimated parameters determined from density functional theory (DFT). For complex surface chemistries, the number of reaction intermediates can be very large, and the cost of calculating the adsorption energies by DFT for all surface intermediates even for one active site model can become prohibitive. In this paper, we have identified appropriate descriptors and machine learning models that can be used to predict a significant part of these adsorption energies given data on the rest of them. Moreover, our investigations also included the case when the species data used to train the predictive model are of different size relative to the species the model tries to predict—this is an extrapolation in the data space which is typically difficult with regular machine learning models. Due to the relative size of the available data sets, we have attempted to extrapolate from the larger species to the smaller ones in the current work. Here, we have developed a neural network based predictive model that combines an established additive atomic contribution based model with the concepts of a convolutional neural network that, when extrapolating, achieves a statistically significant improvement over the previous models.

Computational catalyst discovery involves identification of a meaningful model and suitable descriptors that determine the catalyst properties. We study the impact of combining various descriptors (e.g., reaction energies, metal descriptors, and bond counts) for modeling transition-state energies (TS) based on a database of adsorption and TS energies across transition-metal surfaces for the decarboxylation and decarbonylation of propionic acid, a chemistry characteristic for biomass conversion. Results of different machine learning models for more than 1572 descriptor combinations suggest that there is no statistically significant difference between linear and nonlinear models when using the right combination of reactant energies, metal descriptors, and bond counts. However, linear models are inferior when not including bond count and metal descriptors. Furthermore, when there are missing data for reaction steps on all metals, conventional linear scaling is inferior to linear and nonlinear models with proper choice of descriptors that are surprisingly robust.

Photocatalytic hydrogen peroxide (H2O2) production encounters a major impediment in its low solar-to-chemical conversion (SCC) efficiency due to undesired H2O2product decomposition. Herein, an isolated nickel (Ni) atom modification strategy is developed to adjust the thermodynamic process of H2O2production to address the challenge. Sacrificial experiments and in situ characterization reveal that H2O2generation occurs via a highly selective indirect two-electron oxygen reduction reaction. The optimized photocatalyst exhibits a remarkable H2O2production rate of 338.9 μmol gcat–1h–1in pure water, representing a 48-fold enhancement. Notably, it attains an impressive SCC efficiency of 1.05%, surpassing that of current state-of-the-art catalysts. Theoretical insights reveal the downshifted d-band center facilitates moderate O2adsorption and barrier-free *OOH conversion, favoring H2O2release and preventing *H2O2decomposition. This work showcases efficient H2O2photosynthesis via d-band manipulation, presenting a fresh perspective for advancing high-efficiency SCC systems.

Legume symbiotic nitrogen fixation (SNF) is suppressed by inorganic nitrogen (N) in the soil. High N inhibition of nitrogenase activity is associated with the deprivation of carbon allocation and metabolism in nodules. However, the underlying molecular mechanisms remain unclear. Here, we identify GmCIN1which encodes a cytosolic invertase, as a gateway for the N-tuning of sucrose utilization in nodules. GmCIN1is enriched in mature soybean nodules and its expression is regulated by nitrogen status. The knockout of GmCIN1using genome editing partially mimics the inhibitory effects of N on nitrogenase activity and sugar content and the impact of high N on nodule transcriptomes. This indicates that GmCIN1 partially mediates the high N inhibition of nodule activity. Moreover, ChIP-qPCR and EMSA reveal that SNAP1/2 transcription factors directly bind to the GmCIN1promoter. In addition, SNAP1/2 may be involved in the repression of GmCIN1expression in mature nodules at high N concentrations. Our findings provide insights into the involvement of the transcriptional tuning of carbon (C) metabolism genes by N-signaling modulators in the N-induced inhibition of nitrogenase activity.

As is well known, differential algebraic equations (DAEs), which are able to describe dynamic changes and underlying constraints, have been widely applied in engineering fields such as fluid dynamics, multi-body dynamics, mechanical systems, and control theory. In practical physical modeling within these domains, the systems often generate high-index DAEs. Classical implicit numerical methods typically result in varying order reduction of numerical accuracy when solving high-index systems. Recently, the physics-informed neural networks (PINNs) have gained attention for solving DAE systems. However, it faces challenges like the inability to directly solve high-index systems, lower predictive accuracy, and weaker generalization capabilities. In this paper, we propose a PINN computational framework, combined Radau IIA numerical method with an improved fully connected neural network structure, to directly solve high-index DAEs. Furthermore, we employ a domain decomposition strategy to enhance solution accuracy. We conduct numerical experiments with two classical high-index systems as illustrative examples, investigating how different orders and time-step sizes of the Radau IIA method affect the accuracy of neural network solutions. For different time-step sizes, the experimental results indicate that utilizing a 5th-order Radau IIA method in the PINN achieves a high level of system accuracy and stability. Specifically, the absolute errors for all differential variables remain as low as 10−6, and the absolute errors for algebraic variables are maintained at 10−5. Therefore, our method exhibits excellent computational accuracy and strong generalization capabilities, providing a feasible approach for the high-precision solution of larger-scale DAEs with higher indices or challenging high-dimensional partial differential algebraic equation systems.

Electrochemical conversion of CH4to easily transportable and value-added liquid fuels is highly attractive for energy-efficient CH4utilization, but it is challenging due to the low reactivity and solubility of CH4in the electrolyte. Herein, we report a high-pressure electro-Fenton (HPEF) strategy to establish a hetero-homogeneous process for the electrocatalytic conversion of CH4by O2at room temperature. In combination with elevation of reactant pressure to accelerate reaction kinetics, it delivers an unprecedented HCOOH productivity of 11.5 mmol h–1gFe–1with 220 times enhancement compared to that under ambient pressure. Remarkably, an HCOOH Faradic efficiency of 81.4% can be achieved with an ultralow cathodic overpotential of 0.38 V. The elevated pressure not only promotes the electrocatalytic reduction of O2to H2O2but also increases the reaction collision probability between CH4and •OH, which is in situ generated from the Fe2+-facilitated decomposition of H2O2.

Computational catalyst screening has the potential to significantly accelerate heterogeneous catalyst discovery. Typically, this involves developing microkinetic reactor models that are based on parameters obtained from density functional theory and transition-state theory. To reduce the large computational cost involved in computing various adsorption and transition-state energies of all possible surface states on a large number of catalyst models, linear scaling relations for surface intermediates and transition states have been developed that only depend on a few, typically one or two descriptors, such as the carbon atom adsorption energy. As a result, only the descriptor values have to be computed for various active site models to generate volcano curves in activity or selectivity. Unfortunately, for more complex chemistries the predictability of linear scaling relations is unknown. Also, the selection of descriptors is essentially a trial and error process. Here, using a database of adsorption energies of the surface species involved in the decarboxylation and decarbonylation of propionic acid over eight monometalic transition-metal catalyst surfaces (Ni, Pt, Pd, Ru, Rh, Re, Cu, Ag), we tested if nonlinear machine learning (ML) models can outperform the linear scaling relations in prediction accuracy when predicting the adsorption energy for various species on a metal surface based on data from the rest of the metal surfaces. We found linear scaling relations to hold well for predictions across metals with a mean-absolute error of 0.12 eV, and ML methods being unable to outperform linear scaling relations when the training dataset contains a complete set of energies for all of the species on various metal surfaces. Only when the training dataset is incomplete, namely, contains a random subset of species’ energies for each metal, a currently unlikely scenario for catalyst screening, do kernel-based ML models significantly outperform linear scaling relations. We also found that simple coordinate-free species descriptors, such as bond counts, achieve as good results as sophisticated coordinate-based descriptors. Finally, we propose an approach for automatic discovery of appropriate metal descriptors using principal component analysis.

Hybrid perovskite single crystals offer a great promise for optoelectronic devices, and patterning is broadly required in industrialized applications for functional purposes. However, established patterning techniques meet their limitations when it comes to hybrid perovskite single crystals with multilayered diode structures. In this work, an Ostwald ripening assisted photolithography (ORAP) patterning process, which employs wettability-assisted blade-coating and Ostwald ripening assisted crystallization, is developed for fabricating patterned perovskite single-crystal microarrays. Optoelectronic devices based on uniform perovskite single-crystal microarrays with a multilayered diode structure can be constructed through the ORAP process. To demonstrate the compatibility of the ORAP process with the manufacture of optoelectronic devices, patterned CH3NH3PbBr3single-crystal microarray solar cells, which show enhanced performance than solar cells based on CH3NH3PbBr3continuous single crystals reported, were fabricated. With the development of perovskite research, we are confident that the ORAP process opens a new avenue to fabricate optoelectronic devices based on perovskite microarrays.