Xiamen University of Technology

A summary of the technical advances that are incorporated in the fourth major release of the Q-Chem quantum chemistry program is provided, covering approximately the last seven years. These include developments in density functional theory methods and algorithms, nuclear magnetic resonance (NMR) property evaluation, coupled cluster and perturbation theories, methods for electronically excited and open-shell species, tools for treating extended environments, algorithms for walking on potential surfaces, analysis tools, energy and electron transfer modelling, parallel computing capabilities, and graphical user interfaces. In addition, a selection of example case studies that illustrate these capabilities is given. These include extensive benchmarks of the comparative accuracy of modern density functionals for bonded and non-bonded interactions, tests of attenuated second order Møller–Plesset (MP2) methods for intermolecular interactions, a variety of parallel performance benchmarks, and tests of the accuracy of implicit solvation models. Some specific chemical examples include calculations on the strongly correlated Cr2 dimer, exploring zeolite-catalysed ethane dehydrogenation, energy decomposition analysis of a charged ter-molecular complex arising from glycerol photoionisation, and natural transition orbitals for a Frenkel exciton state in a nine-unit model of a self-assembling nanotube.

A universal but simple procedure for identifying the α, β and γ phases in PVDF using FTIR is proposed and validated. An integrated quantification methodology for individual β and γ phase in mixed systems is also proposed.

Graphitic carbon nitrides and their composites with various morphologies and bandgaps engineered for the hydrogen evolution reaction under visible light are reviewed.

Bioprinting is a process based on additive manufacturing from materials containing living cells. These materials, often referred to as bioink, are based on cytocompatible hydrogel precursor formulations, which gel in a manner compatible with different bioprinting approaches. The bioink properties before, during and after gelation are essential for its printability, comprising such features as achievable structural resolution, shape fidelity and cell survival. However, it is the final properties of the matured bioprinted tissue construct that are crucial for the end application. During tissue formation these properties are influenced by the amount of cells present in the construct, their proliferation, migration and interaction with the material. A calibrated computational framework is able to predict the tissue development and maturation and to optimize the bioprinting input parameters such as the starting material, the initial cell loading and the construct geometry. In this contribution relevant bioink properties are reviewed and discussed on the example of most popular bioprinting approaches. The effect of cells on hydrogel processing and vice versa is highlighted. Furthermore, numerical approaches were reviewed and implemented for depicting the cellular mechanics within the hydrogel as well as for prediction of mechanical properties to achieve the desired hydrogel construct considering cell density, distribution and material-cell interaction.

Diabetes mellitus is a chronic disease characterized by hyperglycemia. It may cause many complications. According to the growing morbidity in recent years, in 2040, the world's diabetic patients will reach 642 million, which means that one of the ten adults in the future is suffering from diabetes. There is no doubt that this alarming figure needs great attention. With the rapid development of machine learning, machine learning has been applied to many aspects of medical health. In this study, we used decision tree, random forest and neural network to predict diabetes mellitus. The dataset is the hospital physical examination data in Luzhou, China. It contains 14 attributes. In this study, five-fold cross validation was used to examine the models. In order to verity the universal applicability of the methods, we chose some methods that have the better performance to conduct independent test experiments. We randomly selected 68994 healthy people and diabetic patients' data, respectively as training set. Due to the data unbalance, we randomly extracted 5 times data. And the result is the average of these five experiments. In this study, we used principal component analysis (PCA) and minimum redundancy maximum relevance (mRMR) to reduce the dimensionality. The results showed that prediction with random forest could reach the highest accuracy (ACC = 0.8084) when all the attributes were used.

Abstract The rapid increase of the CO 2 concentration in the Earth's atmosphere has resulted in numerous environmental issues, such as global warming, ocean acidification, melting of the polar ice, rising sea level, and extinction of species. To search for suitable and capable catalytic systems for CO 2 conversion, electrochemical reduction of CO 2 (CO 2 RR) holds great promise. Emerging heterogeneous carbon materials have been considered as promising metal‐free electrocatalysts for the CO 2 RR, owing to their abundant natural resources, tailorable porous structures, resistance to acids and bases, high‐temperature stability, and environmental friendliness. They exhibit remarkable CO 2 RR properties, including catalytic activity, long durability, and high selectivity. Here, various carbon materials (e.g., carbon fibers, carbon nanotubes, graphene, diamond, nanoporous carbon, and graphene dots) with heteroatom doping (e.g., N, S, and B) that can be used as metal‐free catalysts for the CO 2 RR are highlighted. Recent advances regarding the identification of active sites for the CO 2 RR and the pathway of reduction of CO 2 to the final product are comprehensively reviewed. Additionally, the emerging challenges and some perspectives on the development of heteroatom‐doped carbon materials as metal‐free electrocatalysts for the CO 2 RR are included.

Transformer, the model of choice for natural language processing, has drawn scant attention from the medical imaging community. Given the ability to exploit long-term dependencies, transformers are promising to help atypical convolutional neural networks to learn more contextualized visual representations. However, most of recently proposed transformer-based segmentation approaches simply treated transformers as assisted modules to help encode global context into convolutional representations. To address this issue, we introduce nnFormer (i.e., not-another transFormer), a 3D transformer for volumetric medical image segmentation. nnFormer not only exploits the combination of interleaved convolution and self-attention operations, but also introduces local and global volume-based self-attention mechanism to learn volume representations. Moreover, nnFormer proposes to use skip attention to replace the traditional concatenation/summation operations in skip connections in U-Net like architecture. Experiments show that nnFormer significantly outperforms previous transformer-based counterparts by large margins on three public datasets. Compared to nnUNet, the most widely recognized convnet-based 3D medical segmentation model, nnFormer produces significantly lower HD95 and is much more computationally efficient. Furthermore, we show that nnFormer and nnUNet are highly complementary to each other in model ensembling. Codes and models of nnFormer are available at https://git.io/JSf3i.

Surface-enhanced Raman scattering (SERS) spectroscopy provides a noninvasive and highly sensitive route for fingerprint and label-free detection of a wide range of molecules. Recently, flexible SERS has attracted increasingly tremendous research interest due to its unique advantages compared to rigid substrate-based SERS. Here, the latest advances in flexible substrate-based SERS diagnostic devices are investigated in-depth. First, the intriguing prospect of point-of-care diagnostics is briefly described, followed by an introduction to the cutting-edge SERS technique. Then, the focus is moved from conventional rigid substrate-based SERS to the emerging flexible SERS technique. The main part of this report highlights the recent three categories of flexible SERS substrates, including actively tunable SERS, swab-sampling strategy, and the in situ SERS detection approach. Furthermore, other promising means of flexible SERS are also introduced. The flexible SERS substrates with low-cost, batch-fabrication, and easy-to-operate characteristics can be integrated into portable Raman spectroscopes for point-of-care diagnostics, which are conceivable to penetrate global markets and households as next-generation wearable sensors in the near future.

A MOF-derived composite with multi-functional effects for high-performance Li–S batteries, especially its Co–N dual-catalyzing for S redox.

Toward the pursuit of high-performance Ni2+/Co2+/Fe3+-relevant oxygen evolution reaction (OER) electrocatalysts, the modulation of local electronic structure of the active metal sites provides the fundamental motif, which could be achieved either through direct modifications of local chemical environment or interfacial interaction with a second metal substrate which possesses high electronegativity (typically noble metal Au). Herein, we report that the local electronic structure of Ni–Fe layered double hydroxide (LDH) could be favorably modulated through strong interfacial interactions with FeOOH nanoparticles (NPs). The biphasic and multiscale composites FeOOH/LDH demonstrated an increasingly pronounced synergy effect for OER catalysis when the average size of FeOOH NPs decreases from 18.0 to 2.0 nm. Particularly, the composite with average size of FeOOH NPs of 2.0 nm exhibited an overpotential of 174 mV at 10 mA cm–2 and a tafel slope of 27 mV dec–1 in 1.0 M KOH, outmatching all the noble and non-noble OER catalysts reported so far; it also operates smoothly in various stability tests. A mechanistic study based on XANES and EXAFS analysis, d.c. voltammetry and large amplitude Fourier Transformed a.c. voltammetry proved the presence of high-oxidation-state Fe(3+δ)+sites with relatively short Fe(3+δ)+–O bond from the highly unsaturated ultrafine FeOOH NPs which could reform the local electronic structure and favorably manipulate the electronic oxidation and thus electrocatalytic behaviors of the Ni2+ species in the Ni–Fe LDH, hence leading to the easy formation, excellent OER activity, and extraordinary structural and catalytic stability. Our work puts an emphasis on the role of the solid–solid interfacial chemistry between a Ni–Fe LDH and a non-noble-metal component in engineering the local electronic structure of the active metal sites, which successfully pushed forward the catalytic activity of the well-studied Ni–Fe LDH far beyond its current limit in OER catalysis and opened up an avenue for rational design of OER electrocatalysts.

Video-based human pose recovery is usually conducted by retrieving relevant poses using image features. In the retrieving process, the mapping between 2D images and 3D poses is assumed to be linear in most of the traditional methods. However, their relationships are inherently non-linear, which limits recovery performance of these methods. In this paper, we propose a novel pose recovery method using non-linear mapping with multi-layered deep neural network. It is based on feature extraction with multimodal fusion and back-propagation deep learning. In multimodal fusion, we construct hypergraph Laplacian with low-rank representation. In this way, we obtain a unified feature description by standard eigen-decomposition of the hypergraph Laplacian matrix. In back-propagation deep learning, we learn a non-linear mapping from 2D images to 3D poses with parameter fine-tuning. The experimental results on three data sets show that the recovery error has been reduced by 20%-25%, which demonstrates the effectiveness of the proposed method.

A novel silica-assisted ­polybenzoxazine coating strategy is developed to ­synthesize N-doped hollow porous ­carbon bowls (N-HPCB). Due to the high specific surface area, N atom doping, and unique bowl-like structure of N-HPCB, the S/N-HPCB cathode has stable cycling stability, excellent rate capability, and high volumetric energy density. As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re-organized for online delivery, but are not copy-edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

A newly-designed triboelectric nanogenerator is demonstrated which is composed of a grating-segmented freestanding triboelectric layer and two groups of interdigitated electrodes with the same periodicity. The sliding motion of the grating units across the electrode fingers can be converted into multiple alternating currents through the external load due to the contact electrification and electrostatic induction. Working in non-contact mode, the device shows excellent stability and the total conversion efficiency can reach up to 85% at low operation frequency.

High-performance MoS2 transistors are developed using atomic hexagonal boron nitride as a tunneling layer to reduce the Schottky barrier and achieve low contact resistance between metal and MoS2. Benefiting from the ultrathin tunneling layer within 0.6 nm, the Schottky barrier is significantly reduced from 158 to 31 meV with small tunneling resistance. As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re-organized for online delivery, but are not copy-edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

This Review highlights the recent developments pertaining to pure and modified TiO₂ nanotube arrays for photocatalysis.

We demonstrate a compact Q-switched dual-wavelength erbium-doped fiber (EDF) laser based on graphene as a saturable absorber (SA). By optically driven deposition of graphene on a fiber core, the SA is constructed and inserted into a diode-pumped EDF laser cavity. Also benefiting from the strong third-order optical nonlinearity of graphene to suppress the mode competition of EDF, a stable dual-wavelength Q-switching operation has been achieved using a two-reflection peak fiber Bragg grating as the external cavity mirror. The Q-switched EDF laser has a low pump threshold of 6.5 mW at 974 nm and a wide range of pulse-repetition rate from 3.3 to 65.9 kHz. The pulse duration and the pulse energy have been characterized. This is, to the best of our knowledge, the first demonstration of a graphene-based Q-switched laser.

Porous Cu₂O/CuO polyhedral cages with excellent gas-sensing properties were successfully fabricated by thermal decomposition of Cu-based metal–organic frameworks composed of polyhedral crystals.

The power conversion efficiency of perovskite solar cells (PSCs) has ascended from 3.8% to 22.1% in recent years. ZnO has been well-documented as an excellent electron-transport material. However, the poor chemical compatibility between ZnO and organo-metal halide perovskite makes it highly challenging to obtain highly efficient and stable PSCs using ZnO as the electron-transport layer. It is demonstrated in this work that the surface passivation of ZnO by a thin layer of MgO and protonated ethanolamine (EA) readily makes ZnO as a very promising electron-transporting material for creating hysteresis-free, efficient, and stable PSCs. Systematic studies in this work reveal several important roles of the modification: (i) MgO inhibits the interfacial charge recombination, and thus enhances cell performance and stability; (ii) the protonated EA promotes the effective electron transport from perovskite to ZnO, further fully eliminating PSCs hysteresis; (iii) the modification makes ZnO compatible with perovskite, nicely resolving the instability of ZnO/perovskite interface. With all these findings, PSCs with the best efficiency up to 21.1% and no hysteresis are successfully fabricated. PSCs stable in air for more than 300 h are achieved when graphene is used to further encapsulate the cells.

Abstract Crystal phase regulations may endow materials with enhanced or new functionalities. However, syntheses of noble metal-based allomorphic nanomaterials are extremely difficult, and only a few successful examples have been found. Herein, we report the discovery of hexagonal close-packed Pt–Ni alloy, despite the fact that Pt–Ni alloys are typically crystallized in face-centred cubic structures. The hexagonal close-packed Pt–Ni alloy nano-multipods are synthesized via a facile one-pot solvothermal route, where the branches of nano-multipods take the shape of excavated hexagonal prisms assembled by six nanosheets of 2.5 nm thickness. The hexagonal close-packed Pt–Ni excavated nano-multipods exhibit superior catalytic property towards the hydrogen evolution reaction in alkaline electrolyte. The overpotential is only 65 mV versus reversible hydrogen electrode at a current density of 10 mA cm −2 , and the mass current density reaches 3.03 mA μgPt −1 at −70 mV versus reversible hydrogen electrode, which outperforms currently reported catalysts to the best of our knowledge.

Lithium-sulfur (Li-S) batteries are considered as one of the most potential next-generation rechargeable batteries due to their high theoretical energy density. However, some critical issues, such as low capacity, poor cycling stability, and safety concerns, must be solved before Li-S batteries can be used practically. During the past decade, tremendous efforts have been devoted to the design and synthesis of electrode materials. Benefiting from their tunable structural parameters, hollow porous carbon materials (HPCM) remarkably enhance the performances of both sulfur cathodes and lithium anodes, promoting the development of high-performance Li-S batteries. Here, together with the templated synthesis of HPCM, recent progresses of Li-S batteries based on HPCM are reviewed. Several important issues in Li-S batteries, including sulfur loading, polysulfide entrapping, and Li metal protection, are discussed, followed by a summary on recent research on HPCM-based sulfur cathodes, modified separators, and lithium anodes. After the discussion on emerging technical obstacles toward high-energy Li-S batteries, prospects for the future directions of HPCM research in the field of Li-S batteries are also proposed.