Xiamen University of Technology

The discovery of the enhancement of Raman scattering by molecules adsorbed on nanostructured metal surfaces is a landmark in the history of spectroscopic and analytical techniques. Significant experimental and theoretical effort has been directed toward understanding the surface-enhanced Raman scattering (SERS) effect and demonstrating its potential in various types of ultrasensitive sensing applications in a wide variety of fields. In the 45 years since its discovery, SERS has blossomed into a rich area of research and technology, but additional efforts are still needed before it can be routinely used analytically and in commercial products. In this Review, prominent authors from around the world joined together to summarize the state of the art in understanding and using SERS and to predict what can be expected in the near future in terms of research, applications, and technological development. This Review is dedicated to SERS pioneer and our coauthor, the late Prof. Richard Van Duyne, whom we lost during the preparation of this article.

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.

This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange-correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear-electronic orbital method, and several different energy decomposition analysis techniques. High-performance capabilities including multithreaded parallelism and support for calculations on graphics processing units are described. Q-Chem boasts a community of well over 100 active academic developers, and the continuing evolution of the software is supported by an "open teamware" model and an increasingly modular design.

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 CO2 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 CO2 conversion, electrochemical reduction of CO2 (CO2RR) holds great promise. Emerging heterogeneous carbon materials have been considered as promising metal‐free electrocatalysts for the CO2RR, owing to their abundant natural resources, tailorable porous structures, resistance to acids and bases, high‐temperature stability, and environmental friendliness. They exhibit remarkable CO2RR 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 CO2RR are highlighted. Recent advances regarding the identification of active sites for the CO2RR and the pathway of reduction of CO2 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 CO2RR 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.

Abstract Cancer is one of the leading causes of morbidity and mortality in the world, but more cancer therapies are needed to complement existing regimens due to problems of existing cancer therapies. Herein, we term ferroptosis therapy (FT) as a form of cancer therapy and hypothesize that the FT efficacy can be significantly improved via accelerating the Fenton reaction by simultaneously increasing the local concentrations of all reactants (Fe2+, Fe3+, and H2O2) in cancer cells. Thus, Fenton-reaction-acceleratable magnetic nanoparticles, i.e., cisplatin (CDDP)-loaded Fe3O4/Gd2O3 hybrid nanoparticles with conjugation of lactoferrin (LF) and RGD dimer (RGD2) (FeGd-HN@Pt@LF/RGD2), were exploited in this study for FT of orthotopic brain tumors. FeGd-HN@Pt@LF/RGD2 nanoparticles were able to cross the blood–brain barrier because of its small size (6.6 nm) and LF-receptor-mediated transcytosis. FeGd-HN@Pt@LF/RGD2 can be internalized into cancer cells by integrin αvβ3-mediated endocytosis and then release Fe2+, Fe3+, and CDDP upon endosomal uptake and degradation. Fe2+ and Fe3+ can directly participate in the Fenton reaction, whereas the CDDP can indirectly produce H2O2 to further accelerate the Fenton reaction. The acceleration of Fenton reaction generates reactive oxygen species to induce cancer cell death. FeGd-HN@Pt@LF/RGD2 successfully delivered reactants involved in the Fenton reaction to the tumor site and led to significant inhibition of tumor growth. Finally, the intrinsic magnetic resonance imaging (MRI) capability of the nanoparticles was used to assess and monitor tumor response to FT (self-MRI monitoring).

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.

Toward the pursuit of high-performance Ni 2+ /Co 2+ /Fe 3+ -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 Ni 2+ 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.

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

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.

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.

This Review highlights the recent developments pertaining to pure and modified TiO 2 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 Cu 2 O/CuO polyhedral cages with excellent gas-sensing properties were successfully fabricated by thermal decomposition of Cu-based metal–organic frameworks composed of polyhedral crystals.