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The growth of two-dimensional (2D) materials through chemical vapor deposition (CVD) has sparked a growing interest among both the industrial and academic communities. The interest stems from several key advantages associated with CVD, including high yield, high quality, and high tunability. In order to harness the application potentials of 2D materials, it is often necessary to transfer them from their growth substrates to their desired target substrates. However, conventional transfer methods introduce contamination that can adversely affect the quality and properties of the transferred 2D materials, thus limiting their overall application performance. This review presents a comprehensive summary of the current clean transfer methods for 2D materials with a specific focus on the understanding of interaction between supporting layers and 2D materials. The review encompasses various aspects, including clean transfer methods, post-transfer cleaning techniques, and cleanliness assessment. Furthermore, it analyzes and compares the advances and limitations of these clean transfer techniques. Finally, the review highlights the primary challenges associated with current clean transfer methods and provides an outlook on future prospects.

The increasing seawater temperature during summer months frequently results in severe mortalities in the Pacific oyster Crassostrea gigasaround the world, becoming one of the most significant problems challenging the oyster farming industry. In northern China, significant recurrent summer losses of C. gigashave occurred, and its impact on oyster aquaculture has increased in recent years. Selective breeding for improved oyster resistance to high temperature could help to reduce this massive mortality, but the extent of genetic variation underlying this trait is currently unknown. In this study, we constructed 38 full-sib families using the wild C. gigasand estimated the genetic parameters by performing two month-long high-temperature challenge experiments (30 ℃). Experiment 1 was performed in March 2022 followed by experiment 2 in June 2022 (spawning season). In both challenge experiments, there were significant differences in survival among families, suggesting that C. gigashas a different ability to survive under heat stress. Notably, significantly greater mortality was observed for experiment 2, which related to reproductive status and may contribute to additional stress. Thermal tolerance was defined using both binary test survival and time of death traits. Heritability estimates for thermal tolerance were low to moderate (0.16–0.36 for experiment 1 and 0.16–0.33 for experiment 2) using both a Bayesian (MCMCglmm) and a likelihood-based (ASReml-R) approach and estimated heritability of the threshold animal model using ASReml-R (0.16) appeared to be lower compared to MCMCglmm (0.31–0.32). Notably, the genetic and phenotypic correlations for thermal tolerance between two experiments were 0.463 (BS) to 0.491 (TD) and 0.510 (family survival), respectively, which suggested a significant re-ranking of the family breeding values in different time periods. Finally, the genetic and phenotypic correlations were low between growth traits (shell height, shell length, and shell width) and thermal tolerance, suggesting that selection for these traits should be conducted separately. This study reports the first estimation of genetic parameters for chronic thermal tolerance in C. gigasand indicates that this trait is heritable and selective breeding for thermal tolerance is a feasible and promising approach to reduce summer mortality.

Hyperspectral anomaly detection (HAD) has attracted increasing attention due to its economical and efficient applications. The main challenge lies in the data-starved problem of hyperspectral images (HSIs) and the costliness of manual annotation, making it heavily reliant on the model’s adaptability and robustness to unseen scenes under limited samples. Self-supervised learning offers a solution to this urgency via mining meaningful representations from the data itself. One promising paradigm is leveraging untrained neural networks to reconstruct the background component for revealing anomalous information. Its capability stems from the network architecture and the training process rather than learning from expensive and strongly domain-dependent data, which is naturally applicable to HAD. In this article, to handle the urgent requirement for self-supervised learning in HAD, we propose a multiscale network (termed MSNet) that detects anomalies with enhanced separation training. The network architecture consists of several multiscale convolutional encoder-decoder (CED) layers, considering the spatial characteristics of the anomalies. To suppress the anomalies during background reconstruction, we adopt a new separation training strategy by introducing a soft separator for better practicality on larger datasets. Extensive experiments conducted on five commonly used datasets and the HAD100 dataset, demonstrate the superiority of our method over its counterparts. Our code is available at https://github.com/enter-i-username/MSNet.

Segment anything model (SAM) has been widely applied to various downstream tasks for its excellent performance and generalization capability. However, SAM exhibits three limitations related to remote sensing (RS) semantic segmentation task: 1) the image encoders excessively lose high-frequency information, such as object boundaries and textures, resulting in rough segmentation masks; 2) due to being trained on natural images, SAM faces difficulty in accurately recognizing objects with large-scale variations and uneven distribution in RS images; and 3) the output tokens used for mask prediction are trained on natural images and not applicable to RS image segmentation. In this article, we explore an efficient paradigm for applying SAM to the semantic segmentation of RS images. Furthermore, we propose multiscale enhanced SAM (MeSAM), a new SAM fine-tuning method more suitable for RS images to adapt it to semantic segmentation tasks. Our method first introduces an inception mixer into the image encoder to effectively preserve high-frequency features. Second, by designing a mask decoder with RS correction and incorporating multiscale connections, we make up the difference in SAM from natural images to RS images. Experimental results demonstrated that our method significantly improves the segmentation accuracy of SAM for RS images, outperforming some state-of-the-art (SOTA) methods. The code will be available at https://github.com/Magic-lem/MeSAM.

Now it is still a challenge to compress high-throughput hyperspectral tensor image data on lightweight air-carried/spaceborne remote sensing systems, primarily due to insufficient computational resources and limited transmission bandwidth. To address this challenge, we propose a bit-level tensor data compression network (BTC-Net) that provides higher compression performance by leveraging a data-driven lightweight quantized neural encoder with two-stage bit compression. The BTC-Net achieves semantic near-lossless high reconstruction quality at low compression bit rates thanks to its optimized decoder, which uses a channelwise attention-based enhancement module to recover hyperspectral tensor data. Experimental results on different hyperspectral datasets show that the BTC-Net could achieve an extremely low compression bit rate of fewer than 0.04 bits per pixel per band (bpppb) with the state-of-the-art (SOTA) reconstruction performances. The demo of BTC-Net will be publicly available online at: https://github.com/zx20173646/BTCNet.

Salient object detection (SOD) aims to identify the most prominent regions in images. However, the large model sizes, high computational costs, and slow inference speeds of existing RGB-D SOD models have hindered their deployment on real-world embedded devices. To address this issue, we propose a novel method named AirSOD, which is committed to lightweight RGB-D SOD. Specifically, we first design a hybrid feature extraction network, which includes the first three stages of MobileNetV2 and our Parallel Attention-Shift convolution (PAS) module. Using the novel PAS module enables capturing both long-range dependencies and local information to enhance the representation learning while significantly reducing the number of parameters and computational complexity. Secondly, we propose a Multi-level and Multi-modal feature Fusion (MMF) module to facilitate feature fusion, and a Multi-path enhancement for Feature Refinement (MFR) decoder for feature integration. The proposed method significantly reduces the model size by 63%, decreases the computational complexity by 43%, and improves the inference speed by 43% compared with the cutting-edge model (MobileSal). We test our AirSOD on six widely-used RGB-D SOD datasets. Extensive experimental results demonstrate that our method obtains satisfactory performance. The source codes will be made available.

Characterizing changes in the higher order structure (HOS) of monoclonal antibodies upon stressed conditions is critical to gaining a better understanding of the product and process. One single biophysical approach may not be best suited to assess HOS comprehensively; thus, the synergy from multiple, complementary approaches improves characterization accuracy and resolution. In this study, we employed two mass spectrometry (MS )-based footprinting techniques, namely, fast photochemical oxidation of proteins (FPOP)-MS and hydrogen–deuterium exchange (HDX)-MS, supported by dynamic light scattering (DLS), differential scanning calorimetry (DSC), circular dichroism (CD), and nuclear magnetic resonance (NMR) to study changes to the HOS of a mAb upon thermal stress. The biophysical techniques report a nuanced characterization of the HOS in which CD detects no changes to the secondary or tertiary structure, yet DLS measurements show an increase in the hydrodynamic radius. DSC indicates that the stability decreases, and chemical or conformational changes accumulate with incubation time according to NMR. Furthermore, whereas HDX-MS does not indicate HOS changes, FPOP-MS footprinting reveals conformational changes at residue resolution for some amino acids. The local phenomena observed with FPOP-MS indicate that several residues show various patterns of degradation during thermal stress: no change, an increase in solvent exposure, and a biphasic response to solvent exposure. All evidences show that FPOP-MS efficiently resolves subtle structural changes and novel degradation pathways upon thermal stress treatment at residue-level resolution.

The capability of multistate is critical for electronic synapses in neuromorphic computing. In this article, the multistate performance of the ferroelectric field-effect transistors (FeFETs) is investigated by inserting an Al $_{\mathbf {{2}}}~\text{O}_{\mathbf {{3}}}$ interlayer (IL) into Hf $_{\mathbf {{0}.{5}}}$ Zr0.5O2 film. The experimental results of characterization indicate that the grain sizes of the ferroelectric film can be effectively reduced by the Al $_{\mathbf {{2}}}~\text{O}_{\mathbf {{3}}}$ IL. Benefiting from the refined grain sizes, both the proposed ferroelectric capacitor and FeFET show excellent multistate performance and linearity. The benchmark with other emerging synaptic devices demonstrates that the back-end-of-line (BEOL) compatible FeFETs behave noticeable synaptic performances, such as 160 conductance states, low nonlinearity ( $\alpha _{\mathbf {p}}$ = −1.83 and $\alpha _{\mathbf {d}}$ = −1.39), high $G_{\mathbf {\textit {max}}}$ / $G_{\mathbf {\textit {min}}}$ (46.1), and low energy consumption (1.13 pJ).

Prostate cancer is one of the most commonly diagnosed non-cutaneous malignant tumors and the sixth major cause of cancer-related death generally found in men globally. Automatic segmentation of prostate regions has a wide range of applications in prostate cancer diagnosis and treatment. It is challenging to extract powerful spatial features for precise prostate segmentation methods due to the wide variation in prostate size, shape, and histopathologic heterogeneity among patients. Most of the existing CNN-based architectures often produce unsatisfactory results and inaccurate boundaries in prostate segmentation, which are caused by inadequate discriminative feature maps and the limited amount of spatial information. To address these issues, we propose a novel deep learning technique called Multi-Stage FCN architecture for 2D prostate segmentation that captures more precise spatial information and accurate prostate boundaries. In addition, a new prostate ultrasound image dataset known as CCH-TRUSPS was collected from Chongqing University Cancer Hospital, including prostate ultrasound images of various prostate cancer architectures. We evaluate our method on the CCH-TRUSPS dataset and the publicly available Multi-site T2-weighted MRI dataset using five commonly used metrics for medical image analysis. When compared to other CNN-based methods on the CCH-TRUSPS test set, our Multi-Stage FCN achieves the highest and best binary accuracy of 99.15%, the DSC score of 94.90%, the IoU score of 89.80%, the precision of 94.67%, and the recall of 96.49%. The statistical and visual results demonstrate that our approach outperforms previous CNN-based techniques in all ramifications and can be used for the clinical diagnosis of prostate cancer.

Phycobilisomes (PBSs) are the major photosynthetic light-harvesting complexes in cyanobacteria and red algae. PBS, a multisubunit protein complex, has two major interfaces that comprise intrinsically disordered regions (IDRs): rod–core and core-membrane. IDRs do not form regular, three-dimensional structures on their own. Their presence in the photosynthetic pigment–protein complexes portends their structural and functional importance. A recent model suggests that PB-loop, an IDR located on the PBS subunit ApcE and C-terminal extension (CTE) of the PBS subunit ApcG, forms a structural protrusion on the PBS core-membrane side, facing the thylakoid membrane. Here, the structural synergy between the rod–core region and the core-membrane region was investigated using quantitative mass spectrometry (MS). The AlphaFold-predicted CpcG-CTE structure was first modeled onto the PBS rod–core region, guided and justified by the isotopically encoded structural MS data. Quantitative cross-linking MS analysis revealed that the structural proximity of the PB-loop in ApcE and ApcG-CTE is significantly disturbed in the absence of six PBS rods, which are attached to PBS via CpcG-CTE, indicative of drastic conformational changes and decreased structural integrity. These results suggest that CpcG-rod attachment on the PBS rod–core side is essentially required for the PBS core-membrane structural assembly. The hypothesized long-range synergy between the rod–core interface (where the orange carotenoid protein also functions) and the terminal energy emitter of PBS must have important regulatory roles in PBS core assembly, light-harvesting, and excitation energy transmission. These data also lend strategies that genetic truncation of the light-harvesting antennas aimed for improved photosynthetic productivity must rely on an in-depth understanding of their global structural integrity.

Harris hawks optimization (HHO) has been accepted as one of the well-established swarm-based methods in the community of optimization and machine learning that primarily works based on multiple dynamic features and various exploratory and exploitative traits. Compared with other optimization algorithms, it has been observed that HHO can obtain high-quality solutions for continuous and constrained complex and real-world problems. While there is a wide variety of strategies in the HHO for dealing with diverse situations, there are chances for sluggish performance, where the convergence rate can gradually slow with time, and the HHO may stay stuck in the current relatively better place and may be unable to explore other better areas. To mitigate this concern, this paper combines the Cauchy mutation mechanism into the HHO algorithm named CMHHO. This idea can boost performance and provide a promising optimizer for solving complex optimization problems. The Cauchy mutation mechanism can speed up the convergence of the solution and help HHO explore more promising regions compared to its basic release. On 30 IEEE CEC2017 benchmark functions, the study compared the proposed CMHHO with various conventional and advanced metaheuristics to validate its performance and quality of solutions. It has been found through experiments that the overall optimization performance of CMHHO is far superior to all competitors. The CMHHO method is applied to four engineering challenges to investigate the capabilities of the proposed algorithm in solving real-world problems, and experimental results show that the suggested algorithm is more successful than existing algorithms.Graphical Abstract

Hyperspectral anomaly detection aims to separate sparse anomalies from low-rank background components. A variety of detectors have been proposed to identify anomalies, but most of them tend to emphasize characterizing backgrounds with multiple types of prior knowledge and limited information on anomaly components. To tackle these issues, this article simultaneously focuses on two components and proposes a matrix factorization method with framelet and saliency priors to handle the anomaly detection problem. We first employ a framelet to characterize nonnegative background representation coefficients, as they can jointly maintain sparsity and piecewise smoothness after framelet decomposition. We then exploit saliency prior knowledge to measure each pixel’s potential to be an anomaly. Finally, we incorporate the pure pixel index (PPI) with Reed-Xiaoli’s (RX) method to possess representative dictionary atoms. We solve the optimization problem using a block successive upper-bound minimization (BSUM) framework with guaranteed convergence. Experiments conducted on benchmark hyperspectral datasets demonstrate that the proposed method outperforms some state-of-the-art anomaly detection methods.

The sparse regression method is known for its ability to unmix hyperspectral data, but it can be computationally expensive and accurately insufficient due to the large scale and high coherence of the spectral library. To address this issue, a new approach called layered sparse unmixing termed LSU has been proposed in this article. This method involves breaking down the sparse unmixing process into multilayers, each of which interactively learns a row-sparsity-promoting abundance matrix and fine-tunes active library atoms based on measured activeness. By doing so, LSU outputs both a learned abundance matrix and an optimal library that can best model each mixed pixel in the scene. The proposed LSU can be efficiently solved by the alternating direction method of the multipliers framework. Experimental results obtained from simulated and real hyperspectral images demonstrate the effectiveness of LSU. The demo of the proposed LSU will be publicly available at https://github.com/XiangfeiShen/ Layered_Sparse_Regression_Unmixing.

The sine cosine algorithm (SCA) is a metaheuristic algorithm proposed in recent years that does not resort to nature-related metaphors but explores and exploits the search space with the help of two simple mathematical functions of sine and cosine. SCA has fewer parameters and a simple structure and is widely used in various fields. However, it tends to fall into local optimality because it does not have a well-balanced exploitation and exploration phase. Therefore, in this paper, a new, improved SCA algorithm (QCSCA) is proposed to improve the performance of the algorithm by introducing a quick move mechanism and a crisscross mechanism to SCA and adaptively improving one of the parameters. To verify the effectiveness of QCSCA, comparison experiments with some conventional metaheuristic algorithms, advanced metaheuristic algorithms, and SCA variants are conducted on IEEE CEC2017 and CEC2013. The experimental results show a significant improvement in the convergence speed and the ability to jump out of the local optimum of the QCSCA. The scalability of the algorithm is verified in the benchmark function. In addition, QCSCA is applied to 14 real-world datasets from the UCI machine learning database for selecting a subset of near-optimal features, and the experimental results show that QCSCA is still very competitive in feature selection (FS) compared to similar algorithms. Our experimental results and analysis show that QCSCA is an effective method for solving global optimization problems and FS problems.Graphical Abstract

Carbon-based materials are commonly used supports for platinum (Pt) electrocatalysts in proton-exchange membrane (PEM) fuel cells. However, the limited stability of carbon supports poses a significant challenge in meeting the long lifespan of Pt catalysts. Carbon supports are prone to corrosion in fuel cells, resulting in detachment, aggregation, dissolution, and Ostwald ripening of supported Pt nanoparticles (NPs), leading to electrochemical surface area loss and decreased catalytic activity. In this review, we summarize recent progress in carbon-based catalyst supports and highlight the crucial role of modified carbon supports in oxygen reduction electrocatalysis. A comprehensive evaluation of several carbon support materials shows that support modification is critical for enhanced metal–support interaction and durability. Finally, the proposed perspectives highlight the key features of effective catalyst support for durable electrocatalysts in PEM fuel cells.

Wafer rotation grinding is widely used to obtain thin and ultrathin silicon wafers due to its high precision and efficiency. Residual stress would be introduced in the grinding process, which would cause wafer warpage and even fracture. Raman spectroscopy has long been used to obtain the residual stress states of machined silicon wafers based on the measurement of the frequency shift in the Raman spectra. However, large fluctuations of residual stress values often occur since the measuring point might locate on different micromorphology features, limiting its application. The purpose of this study was to investigate the effect of micromorphological features on the measurement of residual stress using Raman spectroscopy to optimize the residual stress measurement method. The results revealed that the location of the measured points on different micromorphology features has significant impact on the measured residual stress values. The fluctuation shows to be smaller when the measured points are along a line parallel to the scratch marks and larger when the points are along a line perpendicular to the scratch direction. It is recommended that the sampling points should be carefully arranged to obtain more representative values. The study could facilitate the evaluation of residual stress in the grinding process of silicon wafers.

Sustainable development of Hetao Irrigation District (HID), the largest irrigation district in Northwest China, is threaten by soil salinization due to the high evaporation potential, large irrigation amount, and excessive chemical nitrogen fertilizer application. In this study, six nitrogen (N) treatments with application amounts of 0, 50, 100, 150, 200, and 250 kg ha–1in the 2013 season and 0, 100, 150, 200, 250, and 300 kg ha–1in the 2014 season, corresponding to N0, N50, N100, N150, N200, and N250 in the 2013 season and N0, N100, N150, N200, N250, and N300 in the 2014 season, were established to investigate the dynamics of soil salt and NO3-N concentrations, maize yield, and nitrogen use efficiency. The results show that nitrogen application linearly enhanced the soil salt and NO3-N concentrations when N application is higher than 150 kg ha–1. The highest grain yields of maize (16.9–17.1 ton ha–1) in both seasons were found in the N250 treatment, and were 6–9% higher than those for the N300 and N200 treatments and 13–40% higher than those for the N150-N0 treatment. Decreases in the 100-grain weight and grain numbers per ear were the two main reasons for the grain yield reduction in N0-N200 and N300 compared with N250. The maximum water productivity and irrigation water productivity were 3.7 and 4.7 kg m–3, respectively, found in the N250 treatment in both seasons. The nitrogen productivity and partial factor productivity generally decreased with increasing N application. Fully considering the salt balance and maize yield, a nitrogen application amount of 200–250 kg ha–1was recommended for HID.

The metal–vacuum models used to analyze the thermodynamics of the oxygen reduction reaction (ORR) completely overlook the role of electrolytes in the electrochemical process and thus cannot reflect the actual kinetic process occurring at the metal–electrolyte interface. Therefore, based on the real experimental process, the current work elucidates the chemical interactions between the electrolyte and the chemical species for the ORR via a novel metal–electrolyte model for the first time by effectively elucidating the correlation between ORR kinetics and polarization curves. Our simulation model analysis comprises the study of all possible ORR mechanisms on different Pt surfaces (Pt(111), Pt(110), and Pt(100)) and PtNi alloys with different compositions (Pt3Ni(111), Pt2Ni2(111), and PtNi3(111)). The obtained results demonstrate that the hydrogenation of adsorbed oxygen to form adsorbed hydroxyl (R8), whose immense control weight is reflected by a coverage of adsorbed oxygen (θO*) of about 1, is the rate-determining step (RDS) in the four-electron-dominated ORR process. A direct correlation has been established by the great fitting of polarization curves from theoretical ORR kinetics obtained via both the metal–electrolyte model and experimental measurement. This study reveals that among the different Pt surfaces and PtNi alloys, Pt3Ni(111) exhibits the highest ORR activity with the lowest free energy barrier of Ea(0.74 eV), the smallest value of |ΔGO*– 2.46| (0.80 eV), the highest reaction rate r(9.98 × 105s–1per site), and a more positive half-wave potential U1/2(0.93 V). In contrast to previous model studies, this work provides a more accurate theoretical system for catalyst screening, which will help researchers to better understand the experimental phenomena and will be a guiding piece of work for catalyst design and development.

Efficient, robust and cost-effective electrocatalysts that catalyze hydrogen evolution/oxidation reaction (HER/HOR) in alkaline media are highly demanded. Recently, single-atom catalysts (SACs) have emerged as new promising candidates; however, the rational design of supports and the optimization of coordination environment between supports and metal atoms are challenging. In this work, we successfully fabricate atomically dispersed ruthenium (Ru) species, which are strongly coordinated by N and S dual heteroatoms on holey graphene (RuSA/NSG), as an excellent bifunctional catalyst for HER/HOR. In alkaline media, the developed catalyst exhibits high catalytic performance with a low overpotential of 57.3 mV to drive a current density of 10 mA cm−1for HER, and its mass activity is about 5.8 times higher than that of commercial Pt/C and Ru/C catalysts at an overpotential of 100 mV. Similarly, considerable HOR performance of RuSA/NSG is verified to be superior to Pt/C and Ru/C. Furthermore, X-ray-based spectroscopy measurements and density-functional theory calculations have confirmed that, compared with Ru-N4, the tailored Ru-N4-S2with nearby S dopants can act as more active centers to greatly accelerate the sluggish HER/HOR kinetics in alkaline media. The present work provides a new atomic-level engineering strategy to modulate catalytic activities of SACs viathe coordination design using dual heteroatoms on the carbon support.

As one of the most promising methods in the next generation of neuromorphic systems, memristor-based spiking neural networks (SNNs) show great advantages in terms of power efficiency, integration density, and biological plausibility. However, because of the nondifferentiability of discrete spikes, it is difficult to train SNNs with gradient descent and error backpropagation online. In this article, we propose an improved training algorithm for multilayer memristive SNN (MSNN) with three methods spontaneously, supporting in situ learning in hardware: 1) temporal order encoding is applied to generate different pulse trains in neurons; 2) a simplified homeostasis is realized by the activation state and refractory period to regulate hidden neurons spontaneously; and 3) spiking-timing-dependent plasticity (STDP) in memristive synapses is adopted to update weights in situ. Correspondingly, we provide a circuitry example and verify it in LTSPICE. Then the MSNN is benchmarked with the MNIST data set and analyzed with visualization methods, showing better recognition accuracy (95.15%) than existing SNNs with comparable scales and bio-inspired learning rules. We also consider some nonideal effects in memristor crossbar array and peripheral circuits. Evaluation results show that the proposed MSNN is robust to finite resolution, circuit noise and writing noise; and larger network scale will help the MSNN alleviate the negative impacts of other nonideal factors, including yield and device-to-device variation. Moreover, the energy efficiency of a MSNN system is estimated to achieve 7.6TOPS/W, showing great potential in low-power applications.

Crow Search Algorithm (CSA) is a swarm-based single-objective optimizer proposed in recent years, which tried to inspire the behavior of crows that hide foods in different locations and retrieve them when needed. The original version of the CSA has simple parameters and moderate performance. However, it often tends to converge slowly or get stuck in a locally optimal region due to a missed harmonizing strategy during the exploitation and exploration phases. Therefore, strategies of mutation and crisscross are combined into CSA (CCMSCSA) in this paper to improve the performance and provide an efficient optimizer for various optimization problems. To verify the superiority of CCMSCSA, a set of comparisons has been performed reasonably with some well-established metaheuristics and advanced metaheuristics on 15 benchmark functions. The experimental results expose and verify that the proposed CCMSCSA has meaningfully improved the convergence speed and the ability to jump out of the local optimum. In addition, the scalability of CCMSCSA is analyzed, and the algorithm is applied to several engineering problems in a constrained space and feature selection problems. Experimental results show that the scalability of CCMSCSA has been significantly improved and can find better solutions than its competitors when dealing with combinatorial optimization problems. The proposed CCMSCSA performs well in almost all experimental results. Therefore, we hope the researchers can see it as an effective method for solving constrained and unconstrained optimization problems.

The microstructural evolution law and the α variant selection relationship, as well as the deformation mechanism of the HIPed Ti–6Al–4V alloy, were investigated under the conditions of thermal compression and subsequent heat treatment. The results showed that lamellar α phase bending/kinking and fragmentation were the main spheroidizing mechanisms in the α+β phase region (850 °C and 920 °C), DDRX occurred as the restoration mechanism in the near-β region (940 °C), and the heterogeneous microstructure originated from the temperature gradient distribution. The strain rate clearly affected the dynamically recrystallized grain fraction and diameter, e.g … Both the fraction and diameter increased at a lower strain rate. The acicular α precipitated from β phase at 940 °C obeyed the Burgers relationship with β, and their misorientation angle intensified at approximately 60°. GBα formed by reducing the adjacent lamellar α misorientation angle; therefore, the GBα nucleation energy was lowest. The microhardness variation was relatively stable along the X direction at α+β phase region deformation, while oscillation appeared at near-β deformation, which can be explained by microstructure heterogeneity after thermal compression. For heat treatment, the trimodal microstructure appeared at 940 °C/0.001 s−1/60% thermal compression and subsequent 950 °C/1 h/WQ heat treatment, and the result might guide practical production to optimize mechanical properties.

The sodiophilic 3D N/O co-doped Na@CC composite electrode, which aims to suppress the controllable growth of Na dendrites, is designed, manufactured and tested for sodium metal batteries.

The over‐application of cucumber (Cucumis sativusL.) with nitrogen (N) fertilizers grown in greenhouses can produce adverse environmental consequences. In this study, a five‐season experiment was conducted from 2014 to 2018 to determine the influence of N rate on N use and economic returns. The experiment contained traditional N application under furrow irrigation (CK) and three N rates with drip irrigation, they are 100, 75, and 50% N applied in the check, referring to T100, T75, and T50, respectively. However, the irrigation rates in drip irrigation were approximately 40% lower than the check. The N losses from gas emission and leaching nitrate accounted for approximately 70% of the applied N when the traditional N rate was applied (CK and T100); however, the loss reduced to 12% in the T50 treatment. The highest cucumber production was found in the T100 treatment, which was 6.1 and 9.4% higher than the T75 and T50 treatments, respectively. The highest net revenue was found in the T100 treatment, followed by the CK (–5.8%), T75 (–7.6%), and T50 (–9.4%) treatments. The highest N productivity and economically applied N productivity were found in the T50 treatment, which were approximately 60% higher than those in the T100 and CK treatments. Fully considering the economic benefit, N productivity and environmental protection, the local N application for cucumber cultivation in greenhouses on the North China Plain (NCP) can be reduced by 50% with drip irrigation system. The highest cucumber yield and net revenue occurred at the largest N rate with drip irrigation.Fifty percent of traditional N reduced 9.4% net revenue; however, decreased N losses from 70 to 12%.Drip irrigation reduced >40% irrigation water with slight higher net revenue compared to CK.Local N application for cucumber can be reduced by 50% with a drip irrigation system.

All possible natural amino acids have been substituted for the native LeuL185 positioned near the B-side bacteriopheophytin (HB) in the bacterial reaction center (RC) from Rhodobacter sphaeroides. Additional mutations that enhance electron transfer to the normally inactive B-side cofactors are present. Approximately half of the isolated RCs with Glu at L185 contain a magnesium chlorin (CB) in place of HB. The chlorin is not the common BChl aoxidation product 3-desvinyl-3-acetyl chlorophyll awith a C–C bond in ring D and a C═C bond in ring B but has properties consistent with reversal of these bond orders, giving 17,18-didehydro BChl a. In such RCs, charge-separated state P+CB–forms in ∼5% yield. The other half of the GluL185-containing RCs have a bacteriochlorophyll a(BChl a) denoted βBin place of HB. Residues His, Asp, Asn, and Gln at L185 yield RCs with ≥85% βBin the HBsite, while most other amino acids result in RCs that retain HB(≥95%). To the best of our knowledge, neither bacterial RCs that harbor five BChl amolecules and one chlorophyll analogue nor those with six BChl amolecules have been reported previously. The finding that altering the local environment within a cofactor binding site of a transmembrane complex leads to in situ generation of a photoactive chlorin with an unusual ring oxidation pattern suggests new strategies for amino acid control over pigment type at specific sites in photosynthetic proteins.

Perfluorooctanoic acid (PFOA) remains a persistent organic pollutant within aquatic ecosystems. Activated persulfate (PS) technology has been widely used because it can effectively destroy the stable structure of PFOA. However, this technology still has some restrictive problems, such as the large amount of PS required for the reaction and the high SO42-concentration in the wastewater after the reaction. In this study, we used the advantages of hydrodynamic cavitation to activate PS, such as promoting mass transfer and oxidant utilization. After 3hours of treatment, the HC/PS system showed outstanding performance in that 93.6% of PFOA was degraded, and 31.09% of PFOA was defluorinated under optimal conditions with smaller PS dosage and lesser SO42-generation. The different inhibitory effects of coexisting substances (Cl-, CO32-, F-, NO3-, SO42-, HA) on the system were emphasized. The contribution rates of sulfate radicals (SO4•-, 84.4%) and hydroxyl radicals (·OH, 10.9%), which are the main active substances in the HC/PS system, were calculated during the degradation of PFOA. The potential degradation pathways of PFOA were proposed based on the intermediates identified through LC-MS/MS. PFOA gradually lost CF2 units, forming shorter-chain intermediates (PFHpA, PFHxA, PFPeA, PFBA) and F-, ultimately converting to H2O and CO2. Above all, this study provides valuable insights into the future wastewater treatment process for PFOA pollution and has important guiding significance for practical engineering applications.

Wafer rotational grinding is widely used to thin silicon wafers and the grinding-induced residual stress distribution is desired to be known. However, it is difficult to obtain it directly by measurement since the variation of residual stress values of different locations tends to be submerged in the fluctuations of measured values. In this study, molecular dynamics was employed to obtain the residual stress states of ground monocrystalline silicon of different crystal directions. The actual cutting directions of the abrasive grains were obtained as the tangential directions of the abrasive grain's traces on the silicon wafer surface relative to the crystal coordinate system. It was found that the actual cutting direction of an abrasive grain along a single grinding trace would change from the center to the edge of the silicon wafer since the grinding traces are curved lines. The residual stress state at a specific point on the wafer surface was obtained by mapping the residual stress values obtained by molecular dynamics to the actual cutting direction. The residual stress distribution map was obtained successfully and any point of the ground silicon wafer could be known. The location of maximum residual stress was found to deviate from the [1 1 0] crystalline direction on the ground wafer due to the change in the actual cutting direction relative to it.

The cylindrical samples of TC4 titanium alloy prepared by spark plasma sintering (SPS) were compressed with hot deformation of 70% on the thermosimulation machine of Gleeble-1500. The temperature of the processes ranged from 850°C to 1,050°C, and the strain rates varied between 0.001 and 5 s−1. The relative density of the sintered and compressed samples was measured by the Archimedes principle. During hot deformation, the microstructure of the sample was observed. The results show that the average relative density of the samples was 90.2% after SPS. And the relative density was about 98% after the hot deformation of 70%. Under high temperature (>950°C), the sensitivity of flow stress to temperature was reduced. At low strain rate (0.001 s−1), the increase in the deformation temperature promoted the growth of dynamic recrystallization (DRX). At the same temperature, the increase in strain rate slowed down the growth of DRX grains. And the variation tendency was shown from the basket-weave structure to the Widmanstätten structure at a low strain rate (<0.1 s−1), with increase in the strain rate.

The promising alkaline anion exchange membrane fuel cell suffers from sluggish kinetics of the hydrogen oxidation reaction (HOR). However, the puzzling HOR mechanism hinders the further development of highly active catalysts in alkaline media. In this work, we conducted detailed first-principles calculations to acquire a deep understanding of the alkaline HOR mechanism on PtNi bulk alloys [Pt3Ni(111), Pt2Ni2(111), and PtNi3(111)] and its surface alloy [PtNisurf(111)]. The full free energy profiles suggest that the HOR on PtNi alloys proceeds viathe Tafel–Volmer mechanism, that is, the direct decomposition of H2into two adsorbed H, followed by its reaction with OH–in the electrolyte, as the rate-determining step, to form H2O. Therefore, the HOR activity of PtNi alloys is solely impacted by the adsorption of hydrogen, rather than hydroxyl species, though the oxophilicity is also enhanced by alloying Pt with Ni. Thermodynamically, a moderate H adsorption free energy, ΔGH*≈ 0.414 eV, is calculated to be an optimal candidate for the HOR at pH = 13. Alloying Pt with Ni can elevate the d-band center (εd), push the value of ΔGH*closer to 0.414 eV, and thus lower the free energy barrier (Ea) of the rate-determining Volmer reaction, leading to the highest HOR activity of PtNi3(111) among all considered PtNi alloys. This situation is further confirmed by both the microkinetic model and the Tafel plot, where PtNi3(111) exhibits the highest reaction rate (r= 9.42 × 103s–1site–1) and the largest exchange current density (i0= 1.42 mA cm–2) for HOR in alkaline media. This work provides a fundamental understanding of the HOR mechanism and theoretical guidance for rational design of electrocatalysts for HOR in alkaline media.

In the traditional sculpture surface machining process, the G01 code is still the mainstream trajectory. Furthermore, real-time feedrate scheduling and corner smooth algorithm in controller constitute the mainstream method to improve the machining process of short line G01 code in sculpture surface machining. However, the G01 code’s discontinuity and the limits of real-time calculation capacity hinder the use of high-speed machine tools and the accuracy of the machined part. In this article, a new method for sculpture surface machining that considers the advantages and disadvantages of both the computer-aided manufacturing software and the real-time controller is presented to promote the use of a continuous curve tool path. The method mainly transfers the computing-intensive feedrate scheduling and trajectory optimization algorithm in the real-time controller to the computer-aided manufacturing software. Furthermore, the computer-aided manufacturing software generates the machining data, which contain the geometry and feedrate information of the machining process. Finally, the real-time interpolator and the mathematical form of computer-aided manufacturing–generated data are designed simultaneously. In the method, the real-time controller can be designed as simple as possible to release more computing resources to the other real-time intelligent modules. The powerful computational capacity of the software guarantees the optimality of the machining process.

Achieving two-dimensionally (2D) ordered surface wrinkle patterns is still challenging not only for the atomic-thick 2D materials but also in general for all soft surfaces. Normally disordered 2D wrinkle patterns on isotropic surfaces can be rendered via biaxial straining. Here, we report that the 1D and 2D ordered wrinkle patterns in 2D materials can be produced by sequential wrinkling controlled by thermal straining and vertical spatial confinement. The various hierarchical patterns in 2D materials generated by our method are highly periodic, and the hexagonal crystal symmetry is obeyed. More interestingly, these patterns can be maintained in suspended monolayers after delamination from the underlying surfaces which shows the great application potentials. Our new approach can simplify the patterning processes on 2D layered materials and reduce the risk of damage compared to conventional lithography methods, and numerous engineering applications that require nanoscale ordered surface texturing could be empowered.

Utilization of graphene (GE) in water is physically limited by its lack of functional groups, high hydrophobicity, and large interlayer van der Waals forces. Easy agglomeration of GE makes the preparation of GE-filled composites in an aqueous environment very difficult. Conventional methods solve this problem by introducing a surfactant to the system. However, the addition of a large amount of surfactant may hinder the performance of the composite. In this study, highly stable GE aqueous suspensions (remaining stable for more than 30 days) with a high concentration (1.5 mg/mL) were conveniently prepared by using graphene oxide (GO) as a stabilizer. A GE- and GO-filled rubber composite prepared by this GO–GE water suspension in an aqueous environment was also prepared and analyzed. It was found that a dual-network with dual functions was formed in the composite matrix: the GE network enhanced the electrical conductivity of the composite, while the GO network enhanced its mechanical properties. Our method is green, simple, and highly efficient, providing an alternative to improve GE’s dispersion in water and synthesize GE-filled composites in an aqueous environment.

Photosynthesis starts with absorption of light energy by using light-harvesting antenna complexes (LHCs). Overexcitation of LHCs and subsequent photosystems, however, is damaging and can be lethal. The orange carotenoid protein (OCP) protects most cyanobacteria from photodamage by dissipating excessive excitation energy harvested by phycobilisomes (PBS, LHCs) as heat. OCP has two states: the orange, inactive OCP (OCPO) and the red, active OCP (OCPR), with the latter able to bind PBS at a ratio of 2:1 and execute photoprotection. Conversion of OCPOto OCPRis driven by blue light absorption. Previous work indicated that in the presence of Cu2+, photoactivation of OCP can result in it being locked in its red form OCPR. The molecular mechanism of such chemical conversion, however, remains unclear. Here, we demonstrated that Cu+can convert OCPOto OCPRunder anaerobic conditions independent of light illumination. Interestingly, in the presence of Cu2+and ascorbic acid, a ubiquitous reductant in photosynthetic organisms, the conversion of OCPOto OCPRcan also take place spontaneously in the dark, indicative of a locked OCPR–Cu+complex. Furthermore, our functional and structural studies indicate that OCPR–Cu+can interact with PBS and trigger PBS fluorescence quenching. We hypothesize that copper ion, a redox-active component, may synergistically play an important role in the regulation of nonphotochemical quenching in cyanobacteria under stress conditions.