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Artificial intelligence has prevailed in all trades and professions due to the assistance of big data resources, advanced algorithms, and high-performance electronic hardware. However, conventional computing hardware is inefficient at implementing complex tasks, in large part because the memory and processor in its computing architecture are separated, performing insufficiently in computing speed and energy consumption. In recent years, optical neural networks (ONNs) have made a range of research progress in optical computing due to advantages such as sub-nanosecond latency, low heat dissipation, and high parallelism. ONNs are in prospect to provide support regarding computing speed and energy consumption for the further development of artificial intelligence with a novel computing paradigm. Herein, we first introduce the design method and principle of ONNs based on various optical elements. Then, we successively review the non-integrated ONNs consisting of volume optical components and the integrated ONNs composed of on-chip components. Finally, we summarize and discuss the computational density, nonlinearity, scalability, and practical applications of ONNs, and comment on the challenges and perspectives of the ONNs in the future development trends.

P2-type sodium manganese-rich layered oxides are promising cathode candidates for sodium-based batteries because of their appealing cost-effective and capacity features. However, the structural distortion and cationic rearrangement induced by irreversible phase transition and anionic redox reaction at high cell voltage (i.e., >4.0 V) cause sluggish Na-ion kinetics and severe capacity decay. To circumvent these issues, here, we report a strategy to develop P2-type layered cathodes via configurational entropy and ion-diffusion structural tuning. In situ synchrotron X-ray diffraction combined with electrochemical kinetic tests and microstructural characterizations reveal that the entropy-tuned Na 0.62 Mn 0.67 Ni 0.23 Cu 0.05 Mg 0.07 Ti 0.01 O 2 (CuMgTi-571) cathode possesses more {010} active facet, improved structural and thermal stability and faster anionic redox kinetics compared to Na 0.62 Mn 0.67 Ni 0.37 O 2 . When tested in combination with a Na metal anode and a non-aqueous NaClO 4 -based electrolyte solution in coin cell configuration, the CuMgTi-571-based positive electrode enables an 87% capacity retention after 500 cycles at 120 mA g −1 and about 75% capacity retention after 2000 cycles at 1.2 A g −1 . The use of Mn-rich layered cathodes in Na-based batteries is hindered by inadequate cycling reversibility and sluggish anionic redox kinetics. Here, the authors report a strategy to stabilize the structure and promote anionic redox via configurational entropy and ion-diffusion structural tuning.

The occurrence and pollution characteristics of common antibiotics in manure, soil, and sewage sludge in Shenyang, the biggest city in northeastern China, were investigated. Commonly used antibiotics tetracyclines (TCs), including tetracycline, chlortetracycline, oxytetracycline, and deoxytetracycline; and sulfonamides (SAs), including sulfadiazine, sulfamerazine, sulfadimidine, and sulfamethoxazole, were measured by high-performance liquid chromatography–tandem mass spectrometry via solid-phase extraction. Multiple antibiotics could be simultaneously measured in a single manure sample. The highest concentration of antibiotics in manure was 143.97 mg kg −1 (chlortetracycline). There were no significant differences in concentrations of antibiotics between large-scale farms and individual household farms. The concentrations of TCs were significantly higher than those of SAs in manure and soils. However, the concentrations and detection frequencies of antibiotics in soils were significantly lower than in manure. The concentrations of antibiotics varied from 2.56 μg kg −1 of sulfadimidine to 1590.16 μg kg −1 of chlortetracycline, and the detection frequencies were 27.14–64.29 % for TCs and 5.71–28.57 % for SAs in soils. The concentrations of antibiotics in the vegetable field fertilized with manure were higher than those in the corn field not fertilized with manure. Various kinds of antibiotics were detected in sewage sludge, although concentrations were not high. Concentrations of antibiotics were higher in winter than in summer, The concentrations of antibiotics observed in this study were similar to those observed in other regions of China, but, in general, higher than those in other countries.

Future climate change will lead to extreme weather events, such as droughts, which may exacerbate forest fire regimes. However, the impact of future drought characteristics on forest fire regimes has rarely been reported in China. Here, we employed principal component analysis to reduce the dimensionality of drought characteristics, and then used geographically weighted logistic regression models to develop predictive models. These models were applied to future climate simulations under different scenarios to provide projections for different periods, which were then compared with the historical period (2000-2019) to assess the relative changes. We found that the model performed well in its predictions (AUC > 0.75). By comparing the Brier scores, it was found that the models with better predictive performance were those using the SPEI-1 and SPEI-12 timescales. We also found that in the near and medium term of the future, with climate change, the forest fire occurrence probability in most forest land of northern China (NWC, NC, and NEC), especially in Northeast China (NEC), shows an increasing trend, but a decreasing trend in most forest land of southern China (SC, SWC, and EC). Our research can provide a scientific basis for the development of future forest fire management practices that mitigate drought stress according to local conditions.

Magnetic resonance is essential in revealing the structure and dynamics of biomolecules. However, measuring the magnetic resonance spectrum of single biomolecules has remained an elusive goal. We demonstrate the detection of the electron spin resonance signal from a single spin-labeled protein under ambient conditions. As a sensor, we use a single nitrogen vacancy center in bulk diamond in close proximity to the protein. We measure the orientation of the spin label at the protein and detect the impact of protein motion on the spin label dynamics. In addition, we coherently drive the spin at the protein, which is a prerequisite for studies involving polarization of nuclear spins of the protein or detailed structure analysis of the protein itself.

Machine vision faces bottlenecks in computing power consumption and large amounts of data. Although opto-electronic hybrid neural networks can provide assistance, they usually have complex structures and are highly dependent on a coherent light source; therefore, they are not suitable for natural lighting environment applications. In this paper, we propose a novel lensless opto-electronic neural network architecture for machine vision applications. The architecture optimizes a passive optical mask by means of a task-oriented neural network design, performs the optical convolution calculation operation using the lensless architecture, and reduces the device size and amount of calculation required. We demonstrate the performance of handwritten digit classification tasks with a multiple-kernel mask in which accuracies of as much as 97.21% were achieved. Furthermore, we optimize a large-kernel mask to perform optical encryption for privacy-protecting face recognition, thereby obtaining the same recognition accuracy performance as no-encryption methods. Compared with the random MLS pattern, the recognition accuracy is improved by more than 6%.This work highly decreases the size, power, and computation for machine vision with novel joint-optimized opto-electronic neural networks.

Conversion electrodes offer higher reversible capacity and lower cost than conventional intercalation chemistry electrodes, but suffer from kinetic limitation and large volume expansion. Despite significant efforts, developing conversion electrodes with fast charging capability and extended lifespan remains challenging. Here, by leveraging the advantages of high-entropy doping and morphology tailoring, we develop a high-entropy hierarchical micro/nanostructured sulfoselenide Cu 0.88 Sn 0.02 Sb 0.02 Bi 0.02 Mn 0.02 S 0.9 Se 0.1 electrode with entropy-driven fast-charging capability. When used as a negative electrode material for sodium-ion batteries, it achieves a stable cycle life of 10,000 cycles at 30 A g −1 and a high reversible capacity of 365.7 mAh g −1 under fast charging in 13 seconds at 100 A g −1 . Moreover, high-entropy sulfoselenide also demonstrates stable cycling and good rate capability as a positive electrode material for lithium metal batteries, achieving a fast-charging capability of 37 seconds that is comparable with state-of-the-art layered cathodes. High-entropy sulfoselenide is characterized by its robust crystal structure, low ion diffusion barrier, and effective suppression of side reactions with electrolytes during cycling. Importantly, transmission X-ray microscopy affirms the chemical stability of HESSe, which underpins its fast-charging performance. Conversion electrodes with fast charging capability and extended lifespan remains challenging. Here, authors developed Cu 0.88 Sn 0.02 Sb 0.02 Bi 0.02 Mn 0.02 S 0.9 Se 0.1 with entropy-driven fast-charging capability as negative electrode for sodium-ion batteries, enabling fast charging up to 100 A g −1 .

Nitrate pollution in surface water has become a significant environmental concern. Sulfur autotrophic denitrification (SAD) technology is gaining attention for its cost-effectiveness and efficiency in nitrate removal. This study aimed to investigate the structure and function of sulfur autotrophic denitrification microbial communities in systems using sodium thiosulfate (Group A) and elemental sulfur (Group B) as the sole electron donors. Metagenomic amplicon sequencing and physicochemical analysis were performed to examine the microbial communities. The results revealed that on day 13, the nitrate nitrogen removal rate in Group A was significantly higher (89.2%) compared to Group B (74.4%). The dominant genus in both Groups was Thiobacillus , with average abundances of 34.15% and 16.34% in Groups A and B, respectively. β-diversity analysis based on species level showed significant differences in bacterial community structure between the two Groups (P < 0.001). Group A exhibited a greater potential for nitrate reduction and utilized both thiosulfate and elemental sulfur (P < 0.01) compared to Group B. This study provides a sufficient experimental basis for improving the start-up time and operating cost of SAD system through sulfur source switching and offers new prospects for in-depth mechanistic analysis.

Autonomous Boolean networks （ABNs）, which are developed to model the Boolean networks （BNs） with regulatory delays, are well known for their advantages of characterizing the intrinsic evolution rules of biological systems such as the gene regulatory networks. As a special type of ABNs with binary inputs, the autonomous Boolean control networks （ABCNs） are introduced for designing and analyzing the therapeutic intervention strategies where the binary inputs represent whether a certain medicine is dominated or not. An important problem in the therapeutic intervention is to design a control sequence steering an ABCN from an undesirable location （implying a diseased state） to a desirable one （corresponding to a healthy state）. Motivated by such background, this paper aims to investigate the reachability and controllability of ABCNs with pinning controllers. Several necessary and sufficient criteria are provided by resorting to the semi-tensor product tech- niques of matrices. Moreover, an effective pinning control algorithm is presented for steering an ABCN from any given states to the desired state in the shortest time period. Numerical examples are also presented to demonstrate the results obtained.

While lithium–sulfur batteries are poised to be the next-generation high-density energy storage devices, the intrinsic polysulfide shuttle has limited their practical applications. Many recent investigations have focused on the development of methods to wrap the sulfur material with a diffusion barrier layer. However, there is a trade-off between a perfect preassembled wrapping layer and electrolyte infiltration into the wrapped sulfur cathode. Here, we demonstrate an in situ wrapping approach to construct a compact layer on carbon/sulfur composite particles with an imperfect wrapping layer. This special configuration suppresses the shuttle effect while allowing polysulfide diffusion within the interior of the wrapped composite particles. As a result, the wrapped cathode for lithium–sulfur batteries greatly improves the Coulombic efficiency and cycle life. Importantly, the capacity decay of the cell at 1000 cycles is as small as 0.03% per cycle at 1672 mA g –1 . To suppress the polysulfide shuttling effect in Li-S batteries, here the authors report a carbon/sulfur composite cathode with a wrapping layer that overcomes the trade-off between limiting polysulfide diffusion and allowing electrolyte infiltration, and affords extraordinary cycling stability.