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Abstract The East China Sea (ECS) has experienced severe harmful algal blooms (HABs) that have deleterious ecological effects on marine organisms. Recent studies indicated that deploying of a second geostationary ocean color imager (GOCI-II) can significantly improve ocean monitoring. This study systematically assessed GOCI-II and its ability to detect HABs and distinguish between dinoflagellates and diatoms in the ECS. First, the remote-sensing reflectance ( $${R}_{rs}\left(\lambda \right),$$ R rs λ , $$\lambda$$ λ represents the wavelength) obtained from GOCI-II was compared to the local measurement data. Compared to the bands at 412 and 443 nm, the bands at 490, 510, and 620 nm exhibited excellent consistency, which is important for HAB detection. Second, four different methods were employed to extract bloom areas in the ECS: red tide index (RI), spectral shape (SS), red band line height ratio (LHR), and algal bloom ratio ( $${R}_{AB}$$ R AB ). The SS (510) algorithm was the most applicable for detecting blooms from GOCI-II imagery. Finally, the classification capability of GOCI-II for dinoflagellates and diatoms was evaluated using three existing algorithms: the bloom index (BI), combined $$Prorocentrum donghaiens$$ Prorocentrumdonghaiens index (PDI) and diatom index (DI), and the spectral slope ( $${R}_{\_slope}$$ R _ s l o p e ). The BI algorithm yielded more satisfactory results than the other algorithms.

Abstract Background l-ascorbic acid (Asc) plays a pivotal role in regulating various biological processes, including somatic cell reprogramming, through multiple pathways. However, it remains unclear whether Asc regulates reprogramming directly or functions through its metabolites. Results Asc exhibited dual capabilities in promoting reprogramming through both 2,3-diketo-l-gulonic acid (DKG), a key metabolite during Asc degradation, dependent and independent routes. On the one hand, Asc facilitated reprogramming by promoting cell proliferation and inducing the conversion from pre-induced pluripotent stem cells (pre-iPSCs) to iPSCs through DKG-independent pathways. Additionally, Asc triggered mesenchymal-epithelial transition (MET) and activated glycolysis via DKG-dependent mechanisms. Notably, DKG alone activated a non-canonical tricarboxylic acid cycle characterized by increased succinate, fumarate, and malate. Consequently, this shift redirected oxidative phosphorylation toward glycolysis and induced MET. Moreover, owing to its antioxidant capabilities, Asc directly inhibited glycolysis, thereby preventing positive feedback between glycolysis and epithelial-mesenchymal transition, ultimately resulting in a higher level of MET. Conclusion These findings unveil the intricate functions of Asc in the context of reprogramming. This study sheds light on the DKG-dependent and -independent activities of Asc during reprogramming, offering novel insights that may extend the application of Asc to other biological processes.

Abstract Mutation-induced malfunction of ten-eleven translocation methylcytosine dioxygenase 2 (TET2) is widely reported in haematological malignancies. However, the role of TET2 in solid cancers, including colorectal cancer (CRC), is unclear. Here, we found that TET2 malfunction in CRC is mostly due to decreased nuclear localization and that nuclear localization of TET2 is correlated with better survival of patients. To explore the underlying mechanisms, 14 immortalized solid tumour cell lines and 12 primary CRC cell lines were used. TET2 was mostly detected in the nucleus, and it induced significant DNA demethylation and suppressed cell growth by demethylating RORA and SPARC in cell lines like SW480. While in cell lines like SW620, TET2 was observed in the cytosol and did not affect DNA methylation or cell growth. Further examination with immunoprecipitation–mass spectrometry illustrated that β-catenin activation was indispensable for the nuclear localization and tumour suppression effects of TET2. In addition, the β-catenin pathway activator IM12 and the TET2 activator vitamin C were used simultaneously to enhance the effects of TET2 under low-expression conditions, and synergistic inhibitory effects on the growth of cancer were observed both in vitro and in vivo. Collectively, these data suggest that β-catenin-mediated nuclear localization of TET2 is an important therapeutic target for solid tumours.

This study used GOCI-II data to systematically evaluate the feasibility of Rayleigh-corrected reflectance (Rrc) to detect algal blooms in the complex optical environment of the East China Sea (ECS). Based on long-term in situ remote sensing reflectance (Rrs), Rrc spectra demonstrated the similar capability of reflecting the water condition under various atmospheric conditions, and the baseline indices (BLIs) derived from Rrc and Rrs showed good consistency (R2 > 0.98). The effectiveness of five Rrc-based BLIs (SS490, CI, DI, FLH, and MCI) for algal bloom detection was assessed, among which SS490 and MCI showed better performances. A synthetic bloom detection algorithm based on the BLIs of Rrc was then developed to avoid the impact of turbid water. The validation of the BLI algorithm was carried out based on the in situ algal abundance data from 2021 to 2023. Specifically, SS490 showed the best bloom detection result (F-measure coefficient, FM = 0.97), followed by MCI (FM = 0.88). Since the 709 nm bands used in MCI were missing in many ocean color satellites, the SS490 algorithm was more useful in application. Compared to Rrs based bloom detection algorithms, synthetical Rrc BLI proposed in this paper provides more effective observation results and even better algal bloom detection performance. In conclusion, the study confirmed the feasibility of utilizing Rrc for algal bloom detection in the coastal areas of the ECS, and recognized the satisfactory performance of synthetical SS490 by comparing with the other BLIs.

This paper presents a study to evoke rat cortical functional activities, including hemodynamic and neural tissue signal changes, by monopulse electrical stimulation with a microelectrode array using functional optical coherence tomography (fOCT). Based on the principal component analysis and fuzzy clustering method (PCA-FCM), the hemodynamic response of different size blood vessels in rat cortex are analyzed, showing that the hemodynamic response of the superficial large blood vessels is more concentrated. In the regions of neural tissue where blood vessels are removed, positive significant pixels (the intensity of the pixel for five consecutive frames is greater than the average value plus triple standard deviation) and negative significant pixels (the intensity of the pixel for five consecutive frames is less than the average value minus triple standard deviation) exist, and the averaged intensity signal responds rapidly with an onset time of ~20.8 ms. Furthermore, the hemodynamic response was delayed by ~3.5 s from the neural tissue response. fOCT can provide a label-free, large-scale and depth-resolved map of cortical neurovascular activation, which is a promising technology to monitor cortical small-scale neurovascular activities.

An artificial intelligent neural network improved particle swarm optimization algorithm is proposed for the inverse design of semiconductor optical amplifier. Seven input parameters, current-gain curve and saturation output power curve are selected to form the data set based on the physical model of semiconductor optical amplifier. The effectiveness of forecasting performance is improved by contrasting two back propagation neural network techniques (Scaled Conjugate Gradient and Levenberg-Marquardt) and operational settings (Central Processing Unit and Graphics Processing Unit). Higher accuracy is achieved through feedback analysis of neuron number optimization and test error. The addition of a unique backpropagation neural network can make the fitness of particle swarm algorithm mostly converge below $2\times 10^{-4}$. The relative difference between original performances and inverse predictions is close to 0%, which proves the effectiveness of parameter extraction. This method can take advantage of neural networks to improve accuracy and speed of particle swarm optimization algorithms for efficient semiconductor optical amplifier inverse design and multi-solution analysis.

The application of the stereoscopic parking robot makes it possible to relieve the pressure of urban traffic efficiently. With the aggravation of energy and environmental problems, the energy consumption of the stereoscopic parking robot is mainly concerned. In robot design, the kinematic performance reflects the motion transfer capability of the mechanism. A reasonable size of the mechanism can make energy transfer more efficient. This paper proposes a 2-DOF (Degree of Freedom) parallel lifting mechanism of the stereoscopic parking robot. Its branch contains double-constrained triangles that can perform symmetrical movements. Forward kinematics, inverse kinematics, workspace, and singular configuration are studied and analyzed. In this scheme, depending on the characteristics of the mechanism, the evaluation indices of motion consistency, workspace, and singularity performance are proposed. The energy-saving optimization design method based on the multiple kinematic performance indexes is used to find the optimum combination of structural parameters in the feasible region. Finally, the parallel lifting mechanism prototype is constructed, and the dynamic verification experiments are performed at driving speeds of 15.625, 12.5, and 10.42 mm/s. The results provide helpful guidance for the choice of driving speed to achieve optimal movement transfer capacity and stability of the mechanism. The multiple performance index design method can also be applied to other parallel mechanisms with low mobility, which require energy savings and steady motion.

This paper presents an experimental study on the box girder of a low-tower cable-stayed railway bridge with a W-shaped section that consists of prestressed concrete diagonal braces. A 1:6 scale test model was designed and constructed for the experiment. The mechanical behavior of the test model was investigated under two loading conditions: a double-track train symmetrical load and a single-track train unsymmetrical load. The experimental results were validated against a finite element model. Furthermore, the torsional performance of the box girder section was analyzed and discussed.

Soft robots have received a lot of attention because of their great human-robot interaction and environmental adaptability. Most soft robots are currently limited in their applications due to wired drives. Photoresponsive soft robotics is one of the most effective ways to promote wireless soft drives. Among the many soft robotics materials, photoresponsive hydrogels have received a lot of attention due to their good biocompatibility, ductility, and excellent photoresponse properties. This paper visualizes and analyzes the research hotspots in the field of hydrogels using the literature analysis tool Citespace, demonstrating that photoresponsive hydrogel technology is currently a key research direction. Therefore, this paper summarizes the current state of research on photoresponsive hydrogels in terms of photochemical and photothermal response mechanisms. The progress of the application of photoresponsive hydrogels in soft robots is highlighted based on bilayer, gradient, orientation, and patterned structures. Finally, the main factors influencing its application at this stage are discussed, including the development directions and insights. Advancement in photoresponsive hydrogel technology is crucial for its application in the field of soft robotics. The advantages and disadvantages of different preparation methods and structures should be considered in different application scenarios to select the best design scheme.

In coal and rock recognition technology, the acquisition of sound signals is affected by background noise. It is challenging to extract cutting features and accurately identify cutting patterns effectively. Therefore, this paper proposes an approach for combined noise reduction of the cutting sound signal based on the improved adaptive noise complete ensemble empirical mode decomposition (ICEEMDAN) and a singular value decomposition (SVD). First, the method used the ICEEMDAN method to decompose the noisy signal into several intrinsic mode functions (IMF). It calculated the correlation coefficient between the IMF component and the noisy signal and then selected the noisy IMF components based on the threshold formula. Meanwhile, this method constructed a Hankel matrix of the noisy IMF component signals. It used SVD technology to obtain the singular values. According to the singular value standard energy spectrum curve, the paper determined the order of the effective singular value and removed the noise component in the signal. Then, the denoised IMF and noiseless IMF components are superimposed and reconstructed to obtain the noise-reduced cutting sound signal. Finally, it applied simulation signal and simulated shearer cutting experiment to verify the performance of the method. The results show that the proposed method can effectively remove the influence of background noise in the signal and retain the characteristic frequencies of the original cutting sound signal. Compared with traditional noise reduction methods, the ICEEMDAN-SVD combined noise reduction method performs better in noise reduction evaluation standards of signal-noise ratio and root mean square error. It achieved a better noise reduction effect, which could help coal and rock recognition technology based on sound signals.

Abstract Retinoic acid (RA) and 2-phospho-L-ascorbic acid trisodium salt (AscPNa) promote the reprogramming of mouse embryonic fibroblasts to induced pluripotent stem cells. In the current studies, the lower abilities of RA and AscPNa to promote reprogramming in the presence of each other suggested that they may share downstream pathways at least partially. The hypothesis was further supported by the RNA-seq analysis which demonstrated a high-level overlap between RA-activated and AscPNa activated genes during reprogramming. In addition, RA upregulated Glut1/3, facilitated the membrane transportation of dehydroascorbic acid, the oxidized form of L-ascorbic acid, and subsequently maintained intracellular L-ascorbic acid at higher level and for longer time. On the other hand, AscPNa facilitated the mesenchymal-epithelial transition during reprogramming, downregulated key mesenchymal transcriptional factors like Zeb1 and Twist1, subsequently suppressed the expression of Cyp26a1/b1 which mediates the metabolism of RA, and sustained the intracellular level of RA. Furthermore, the different abilities of RA and AscPNa to induce mesenchymal-epithelial transition, pluripotency, and neuronal differentiation explain their complex contribution to reprogramming when used individually or in combination. Therefore, the current studies identified a positive feedback between RA and AscPNa, or possibility between vitamin A and C, and further explored their contributions to reprogramming.

Multi-set canonical correlation analysis (MCCA) is a famous multi-modal coherent subspace learning method. However, sample-based between-modal and within-modal covariance matrices of MCCA usually deviate from real covariance matrices due to noise information and limited sample size. The deviation will weaken the performance of MCCA, especially in image recognition. Aiming at this challenging issue, we correct singular values of sample covariance matrices with the employment of Cauchy estimate theory and further obtain Cauchy covariance matrices that are closer to real covariance matrices. On the basis of Cauchy covariance matrices, we develop a novel multi-modal subspace fusion method, i.e. Cauchy multi-set canonical correlations. By maximizing Cauchy correlations between different modalities and constraining Cauchy scatters of within-modal data, the method can learn a Cauchy coherent fusion subspace with well discriminative power from a few images. Experiment results have shown the effectiveness of the proposed method, promising to the aims of this research.

The coast of the East China Sea (ECS) is one of the regions most frequently affected by harmful algal blooms in China. Remote sensing monitoring could assist in understanding the mechanism of blooms and their associated environmental changes. Based on imagery from the Second-Generation Global Imager (SGLI) conducted by Global Change Observation Mission-Climate (GCOM-C) (Japan), the accuracy of satellite measurements was initially validated using matched pairs of satellite and ground data relating to the ECS. Additionally, using SGLI data from the coast of the ECS, we compared the applicability of three bloom extraction methods: spectral shape, red tide index, and algal bloom ratio. With an RMSE of less than 25%, satellite data at 490 nm, 565 nm, and 670 nm showed good consistency with locally measured remote sensing reflectance data. However, there was unexpected overestimation at 443 nm of SGLI data. By using a linear correction method, the RMSE at 443 nm was decreased from 27% to 17%. Based on the linear corrected SGLI data, the spectral shape at 490 nm was found to provide the most satisfactory results in separating bloom and non-bloom waters among the three bloom detection methods. In addition, the capability in harmful algae distinguished using SGLI data was discussed. Both of the Bloom Index method and the green-red Spectral Slope method were found to be applicable for phytoplankton classification using SGLI data. Overall, the SGLI data provided by GCOM-C are consistent with local data and can be used to identify bloom water bodies in the ECS, thereby providing new satellite data to support monitoring of bloom changes in the ECS.

Additive manufacturing (AM), also known as three-dimensional printing, is gaining increasing attention from academia and industry due to the unique advantages it has in comparison with traditional subtractive manufacturing. However, AM processing parameters are difficult to tune, since they can exert a huge impact on the printed microstructure and on the performance of the subsequent products. It is a difficult task to build a process–structure–property–performance (PSPP) relationship for AM using traditional numerical and analytical models. Today, the machine learning (ML) method has been demonstrated to be a valid way to perform complex pattern recognition and regression analysis without an explicit need to construct and solve the underlying physical models. Among ML algorithms, the neural network (NN) is the most widely used model due to the large dataset that is currently available, strong computational power, and sophisticated algorithm architecture. This paper overviews the progress of applying the NN algorithm to several aspects of the AM whole chain, including model design, in situ monitoring, and quality evaluation. Current challenges in applying NNs to AM and potential solutions for these problems are then outlined. Finally, future trends are proposed in order to provide an overall discussion of this interdisciplinary area. Keywords: Additive manufacturing, 3D printing, Neural network, Machine learning, Algorithm

Visible Infrared Imaging Radiometer Suite (VIIRS) data were systematically evaluated and used to detect harmful algal bloom (HAB) and classify algal bloom types in coasts of the East China Sea covered by optically complex and sediment-rich waters. First, the accuracy and spectral characteristics of VIIRS retrieved normalized water-leaving radiance or the equivalent remote sensing reflectance from September 2019 to October 2020 that were validated by the long-term observation data acquired from an offshore platform and underway measurements from a cruise in the Changjiang Estuary and adjacent East China Sea. These data were evaluated by comparing them with data from the Moderate-Resolution Imaging Spectroradiometer. The bands of 486, 551, and 671 nm provided much higher quality than those of 410 and 443 nm and were more suitable for HAB detection. Secondly, the performance of four HAB detection algorithms were compared. The Ratio of Algal Bloom (RAB) algorithm is probably more suitable for HAB detection in the study area. Importantly, although RAB was also verified to be applicable for the detection of different kinds of HAB (Prorocentrum donghaiense, diatoms, Ceratium furca, and Akashiwo sanguinea), the capability of VIIRS in the classification of those algal species was limited by the lack of the critical band near 531 nm.

Summary: Normally, opioids function in a receptor-dependent manner. They bind to opioid receptors, activate or inhibit receptor activation, and subsequently modulate downstream signal transduction. However, the complex functions of opioids and the low expression of opioid receptors and their endogenous peptide agonists in neural stem cells (NSCs) suggest that some opioids may also modulate NSCs via a receptor-independent pathway. In the current study, two opioids, morphine and naloxone, are demonstrated to facilitate NSC proliferation via a receptor-independent and ten-eleven translocation methylcytosine dioxygenase 1 (TET1)-dependent pathway. Morphine and naloxone penetrate cell membrane, bind to TET1 protein via three key residues (1,880–1,882), and subsequently result in facilitated proliferation of NSCs. In addition, the two opioids also inhibit the DNA demethylation ability of TET1. In summary, the current results connect opioids and DNA demethylation directly at least in NSCs and extend our understanding on both opioids and NSCs. : Liang et al. demonstrate a receptor-independent and Tet1-dependent pathway used by opioids naloxone and morphine to facilitate the proliferation of NSCs. The binding of naloxone and morphine to TET1 suggests that DNA methylation should be considered when studying the tolerance- and addiction-induced opioids. Keywords: neural stem cells, naloxone, morphine, Tet1, receptor-independent, proliferation

When the shearer is cutting, the sound signal generated by the cutting drum crushing coal and rock contains a wealth of cutting status information. In order to effectively process the shearer cutting sound signal and accurately identify the cutting mode, this paper proposed a shearer cutting sound signal recognition method based on an improved complete ensemble empirical mode decomposition with adaptive noise (ICCEMDAN) and an improved grey wolf optimizer (IGWO) algorithm-optimized support vector machine (SVM). First, the approach applied ICEEMDAN to process the cutting sound signal and obtained several intrinsic mode function (IMF) components. It used the correlation coefficient to select the characteristic component. Meanwhile, this paper calculated the composite multi-scale permutation entropy (CMPE) of the characteristic components as the eigenvalue. Then, the method introduced a differential evolution algorithm and nonlinear convergence factor to improve the GWO algorithm. It used the improved GWO algorithm to realize the adaptive selection of SVM parameters and established a cutting sound signal recognition model. According to the proportioning plan, the paper made several simulation coal walls for cutting experiments and collected cutting sound signals for cutting pattern recognition. The experimental results show that the method proposed in this paper can effectively process the cutting sound signal of the shearer, and the average accuracy of the cutting pattern recognition model reached 97.67%.

When the shearer cuts coal or rock with different hardness, it will produce corresponding cutting state information. This paper develops a simulation cutting experiment system for the drum shearer based on similarity theory. It took the spiral cutting drum of a shearer as the research target and derived the principal similarity coefficients through the dimensional analysis method. Meanwhile, this paper designed the structure of the cutting power system and hydraulic system. Then, it chose a certain amount of coal powder as an aggregate, cement 325# as cementing material, sand, and water as auxiliary materials to prepare simulated coal samples. The paper adopted the orthogonal experiment method and used a proportion of cement, sand, and water as the influencing factors in designing a simulated coal sample preparation plan. In addition, it utilized the range analysis method to research the influence of various factors on the density and compressive strength of simulated coal samples. Finally, it conducted simulated coal sample cutting tests. The results show that the density of the simulated coal samples is between 1192.59 Kg/m3–1483.51 Kg/m3, and the compressive strength range reaches 0.16 MPa–3.94 MPa. The density of the simulated coal sample is related to the mass proportion of cement and sand. When the ratio gradually increases, the influence of sand increases. Furthermore, the compressive strength is linearly proportional to the proportion of cement. The self-designed simulation cutting experiment system could effectively carry out the relevant experiments and obtain the corresponding cutting condition signals through the sensors. There are differences in vibration signals generated by cutting different strength materials. Extracting the kurtosis value as the characteristic value can distinguish various cutting modes, which can provide a reliable experimental solution for the research of coal-rock identification.

During operation, the acoustic signal of the drum shearer contains a wealth of information. The monitoring or diagnosis system based on acoustic signal has obvious advantages. However, the signal is challenging to extract and recognize. Therefore, this paper proposes an approach for acoustic signal processing of a shearer based on the parameter optimized variational mode decomposition (VMD) method and a clustering algorithm. First, the particle swarm optimization (PSO) algorithm searched for the best parameter combination of the VMD. According to the results, the approach determined the number of modes and penalty parameters for VMD. Then the improved VMD algorithm decomposed the acoustic signal. It selected the ideal component through the minimum envelope entropy. The PSO was designed to optimize the clustering analysis, and the minimum envelope entropy of the acoustic signal was regarded as the feature for classification. We then use a shearer simulation platform to collect the acoustic signal and use the approach proposed in this paper to process and classify the signal. The experimental results show that the approach proposed can effectively extract the features of the acoustic signal of the shearer. The recognition accuracy of the acoustic signal was high, which has practical application value.

Abstract Thermoelectrics converting heat and electricity directly attract broad attentions. To enhance the thermoelectric figure of merit, zT, one of the key points is to decouple the carrier-phonon transport. Here, we propose an entropy engineering strategy to realize the carrier-phonon decoupling in the typical SrTiO3-based perovskite thermoelectrics. By high-entropy design, the lattice thermal conductivity could be reduced nearly to the amorphous limit, 1.25 W m−1 K−1. Simultaneously, entropy engineering can tune the Ti displacement, improving the weighted mobility to 65 cm2 V−1 s−1. Such carrier-phonon decoupling behaviors enable the greatly enhanced μ W/κ L of ~5.2 × 103 cm3 K J−1 V−1. The measured maximum zT of 0.24 at 488 K and the estimated zT of ~0.8 at 1173 K in (Sr0.2Ba0.2Ca0.2Pb0.2La0.2)TiO3 film are among the best of n-type thermoelectric oxides. These results reveal that the entropy engineering may be a promising strategy to decouple the carrier-phonon transport and achieve higher zT in thermoelectrics.

Abstract Anion exchange membrane fuel cells (AEMFCs) have been hailed as a promising hydrogen energy technology due to high energy conversion efficiency, zero carbon emission and the potential independence on scare and expensive noble metal electrocatalysts. A variety of platinum group metal (PGM)‐free catalysts has been developed with superior catalytic performance to noble metal benchmarks toward cathodic oxygen reduction reactions (ORR). However, PGM electrocatalysts still dominate the anodic catalyst research because the kinetics of hydrogen oxidation reaction (HOR) are two or three orders of magnitude slower than in that acidic media. Therefore, it is urgently desirable to improve noble metal utilization efficiency and/or develop high‐performance PGM‐free electrocatalysts for HOR, thus promoting the real‐world implementation of AEMFCs. In this review, the current research progress of electrocatalysts for HOR in alkaline media is summarized. We start with the discussion on the current HOR reaction mechanisms and existing controversies. Then, methodologies to improve the HOR performance are reviewed. Following these principles, the recently developed HOR electrocatalysts including PGM and PGM‐free HOR electrocatalysts in alkaline media are systematically introduced. Finally, we put forward the challenges and prospects in the field of HOR catalysis.

Abstract The limited durability of metal-nitrogen-carbon electrocatalysts severely restricts their applicability for the oxygen reduction reaction in proton exchange membrane fuel cells. In this study, we employ the chemical vapor modification method to alter the configuration of active sites from FeN4 to the stable monosymmetric FeN2+N’2, along with enhancing the degree of graphitization in the carbon substrate. This improvement effectively addresses the challenges associated with Fe active center leaching caused by N-group protonation and free radicals attack due to the 2-electron oxygen reduction reaction. The electrocatalyst with neoteric active site exhibited excellent durability. During accelerated aging test, the electrocatalyst exhibited negligible decline in its half-wave potential even after undergoing 200,000 potential cycles. Furthermore, when subjected to operational conditions representative of fuel cell systems, the electrocatalyst displayed remarkable durability, sustaining stable performance for a duration exceeding 248 h. The significant improvement in durability provides highly valuable insights for the practical application of metal-nitrogen-carbon electrocatalysts.

The usage of cheap crude H2 in proton-exchange membrane fuel cells (PEMFCs) is still unrealistic to date, due to the suffering of the current Pt based nano-catalysts from impurities such as CO in anode. Recently, synergistic active sites between single atom (SA) and nanoparticle (NP) have been found to be promising for overcoming the poisoning problem. However, lengthening the nanoparticle-single atom (SA–NP) interface, i.e., constructing high density synergistic active sites, remains highly challenging. Herein, we present a new strategy based on molecular fusion strategy to create abundant SA–NP interfaces, with high density SA–NP interfaces created on a two dimensional nitrogen doped carbon nanosheets (Ir-SACs&NPs/NC). Owing to the abundance of SA–NP interface sites, the catalyst was empowered with a high tolerance towards up to 1000 ​ppm CO in H2 feed. These findings provide guidelines for the design and construction of active and anti-poisoning catalysts for PEMFC anode.

Abstract Background Deep learning (DL), a specialized form of machine learning (ML), is valuable for forecasting survival in various diseases. Its clinical applicability in real-world patients with gastric cancer (GC) has yet to be extensively validated. Methods A combined cohort of 11,414 GC patients from the Surveillance, Epidemiology and End Results (SEER) database and 2,846 patients from a Chinese dataset were utilized. The internal validation of different algorithms, including DL model, traditional ML models, and American Joint Committee on Cancer (AJCC) stage model, was conducted by training and testing sets on the SEER database, followed by external validation on the Chinese dataset. The performance of the algorithms was assessed using the area under the receiver operating characteristic curve, decision curve, and calibration curve. Results DL model demonstrated superior performance in terms of the area under the curve (AUC) at 1, 3, and, 5 years post-surgery across both datasets, surpassing other ML models and AJCC stage model, with AUCs of 0.77, 0.80, and 0.82 in the SEER dataset and 0.77, 0.76, and 0.75 in the Chinese dataset, respectively. Furthermore, decision curve analysis revealed that the DL model yielded greater net gains at 3 years than other ML models and AJCC stage model, and calibration plots at 3 years indicated a favorable level of consistency between the ML and actual observations during external validation. Conclusions DL-based model was established to accurately predict the survival rate of postoperative patients with GC.

Abstract Understanding the lattice dynamics and heat transport physics in the lead-free halide double perovskites remains an outstanding challenge due to their lattice dynamical instability and strong anharmonicity. In this work, we investigate the microscopic mechanisms of anharmonic lattice dynamics and thermal transport in lead-free halide double perovskite Cs2AgBiBr6 from first principles. We combine self-consistent phonon calculations with bubble diagram correction and a unified theory of lattice thermal transport that considers both the particle-like phonon propagation and wave-like tunnelling of phonons. An ultra-low thermal conductivity at room temperature (~0.21 Wm−1K−1) is predicted with weak temperature dependence( ~ T −0.34), in sharp contrast to the conventional ~T −1 dependence. Particularly, the vibrational properties of Cs2AgBiBr6 are featured by strong anharmonicity and wave-like tunnelling of phonons. Anharmonic phonon renormalization from both the cubic and quartic anharmonicities are found essential in precisely predicting the phase transition temperature in Cs2AgBiBr6 while the negative phonon energy shifts induced by cubic anharmonicity has a significant influence on particle-like phonon propagation. Further, the contribution of the wave-like tunnelling to the total thermal conductivity surpasses that of the particle-like propagation above around 310 K, indicating the breakdown of the phonon gas picture conventionally used in the Peierls-Boltzmann Transport Equation. Importantly, further including four-phonon scatterings is required in achieving the dominance of wave-like tunnelling, as compared to the dominant particle-like propagation channel when considering only three-phonon scatterings. Our work highlights the importance of lattice anharmonicity and wave-like tunnelling of phonons in the thermal transport in lead-free halide double perovskites.

Abstract Although first principles based anharmonic lattice dynamics is one of the most common methods to obtain phonon properties, such method is impractical for high-throughput search of target thermal materials. We develop an elemental spatial density neural network force field as a bottom-up approach to accurately predict atomic forces of ~80,000 cubic crystals spanning 63 elements. The primary advantage of our indirect machine learning model is the accessibility of phonon transport physics at the same level as first principles, allowing simultaneous prediction of comprehensive phonon properties from a single model. Training on 3182 first principles data and screening 77,091 unexplored structures, we identify 13,461 dynamically stable cubic structures with ultralow lattice thermal conductivity below 1 Wm−1K−1, among which 36 structures are validated by first principles calculations. We propose mean square displacement and bonding-antibonding as two low-cost descriptors to ease the demand of expensive first principles calculations for fast screening ultralow thermal conductivity. Our model also quantitatively reveals the correlation between off-diagonal coherence and diagonal populations and identifies the distinct crossover from particle-like to wave-like heat conduction. Our algorithm is promising for accelerating discovery of novel phononic crystals for emerging applications, such as thermoelectrics, superconductivity, and topological phonons for quantum information technology.

The electrochemical carbon dioxide reduction (ECR) to profoundly diminished chemical entities offers a compelling avenue for transforming sporadic energy resources into enduring fuels while forging an enclosed anthropogenic carbon cycle. Metal-organic frameworks (MOFs) has been extensively investigated as a promising multifunctional material for ECR. Notably, two-dimensional (2D) MOFs attract particular research attention due to their specific chemical and structural properties, i.e., enhanced electrical conductivity, increased open sites, improved mass transport and tunable interfacial environments. In this review, the recent progress of 2D MOFs for ECR is summarized. We begin with the introduction of the synthetic strategies of 2D MOFs. Then, we mainly focus on the advanced 2D MOF electrocatalysts for ECR in recent years, which are clarified by the products. The mechanism underlying the conversion of carbon dioxide (CO2) into carbon products, the factors influencing product formation and a summary of selected 2D MOF catalysts and their synthetic methods are presented. By consolidating the potential factors contributing to the products, we anticipate that the review will offer fresh opportunities for further advancements in CO2 reduction with 2D MOF catalysts.