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High-entropy perovskite thin films, as the prototypical representative of the high-entropy oxides with novel electrical and magnetic features, have recently attracted great attention. Here, we reported the electronic structure and charge transport properties of sol-gel-derived high-entropy Ba(Zr 0.2 Sn 0.2 Ti 0.2 Hf 0.2 Nb 0.2)O 3 thin films annealed at various temperatures. By means of X-ray photoelectron spectroscopy and absorption spectrum, it is found that the conduction-band-minimum shifts downward and the valence-band-maximum shifts upward with the increase of annealing temperature, leading to the narrowed band gap. Electrical resistance measurements confirmed a semiconductor-like behavior for all the thin films. Two charge transport mechanisms, i.e. , the thermally-activated transport mechanism at high temperatures and the activation-less transport mechanism at low temperatures, are identified by a self-consistent analysis method. These findings provide a critical insight into the electronic band structure and charge transport behavior of Ba(Zr 0.2 Sn 0.2 Ti 0.2 Hf 0.2 Nb 0.2)O 3 , validating it as a compelling high-entropy oxide material for future electronic/energy-related technologies. [ABSTRACT FROM AUTHOR]

• Smartphones show excellent ability to predict original soil properties. • Image segmentation significantly improves soil property prediction accuracy. • Significant differences in dominant regions between segmentation methods. • The K-means clustering segmentation method outperforms the OUST method. Prediction of soil properties based on images of original soil acquired from smartphones is considered a potential alternative to conventional soil property determination due to its convenience and low cost. However, images of the original soil may contain distracting information such as fissures, shadows, and rocks, which may affect the image feature extraction and property prediction results. Four unsupervised image segmentation methods, namely the maximum interclass variance (OUST) method, the local adaptive threshold segmentation (LATS) method, the K-means clustering segmentation (KMCS) method, and the natural break point segmentation (NBPS) method, were used to segment 62 images of original soil taken by smartphones in outdoor sunny conditions. The number of segmentation levels (N) for KMCS and NBPS varied from 2 to 10. The dominant region, determined as the region with the highest percentage of image area after segmentation, was selected for subsequent property prediction. The results of the segmentation methods revealed significant differences in the dominant regions, leading to significant variations in color and texture features (p < 0.05). The random forest approach was used to construct predictive models for soil Munsell colour, soil organic matter (SOM) content, and soil texture content. The validation results demonstrated that the image segmentation methods improved the prediction accuracy of all three soil properties compared to that of the unsegmented images. Among the segmentation methods, KMCS (N = 3 and 4) exhibited the highest segmentation performance, surpassing the widely used OUST method. This research highlights the significance of image segmentation methods in predicting soil properties based on images of original soil samples and recommended it as an essential process for future soil image processing. The findings of this study have the potential to advance the development of smartphone-based prediction of properties of original soils. [ABSTRACT FROM AUTHOR]

In this study, we plan to utilize the plentiful oxygen-containing functional groups present on the surface of GO as an anchoring point for [PtCl 6 ]2−, thereby creating a highly dispersible electroplating material. [Display omitted] • Construction of [PtCl 6 ]2−-GO colloid based on anchoring strategy. • [PtCl 6 ]2−-GO colloid are utilized in a variety of electrodes. • Cluster-scale PtNPs (3 nm) were grown on the rGO surface under mild conditions. • The Pt-rGO-modified electrodes provide excellent sensing and HER performance. In this study, a bifunctional [PtCl 6 ]2−-GO colloidal material was prepared by anchoring [PtCl 6 ]2− by abundant oxygen-containing functional groups in protonated graphene oxide. Two modification strategies, surface overcoating and electrochemical in situ deposition, were adopted to modify the screen-printed carbon electrode (SPCE), foam electrodes (CuF, NiF), respectively, and Pt-rGO, which uniformly covered the electrode surface, was obtained by a simple onestep of reducing [PtCl 6 ]2−-GO. The effectiveness of the modified materials and electrode modification strategies prepared was validated by tinidazole electrochemical detection and hydrogen evolution reaction (HER). The results indicated that the Pt-rGO modified SPCE possessed lower LOD and higher sensitivity for the detection. Furthermore, as for HER, optimized Pt-rGO-modified NiF and CuF required only 26 mV and 76 mV overpotentials in alkaline and acidic solutions, respectively, to obtain current densities of 10 mA cm−2. This was largely ascribed to the synergistic interaction between platinum and rGO, with the synthesized platinum-rGO displaying stronger active sites and higher intrinsic activity. In particular, Pt, which acts as an active center, could offer part of its electron to H 2 O and adjacent reduced graphene oxide sites, thus promoting strong electrical connections and efficient HER kinetics. [ABSTRACT FROM AUTHOR]

Semiconductor metal oxides (SMOs) nanomaterials are a category of sensing materials that are widely applied to chemiresistive NO x gas sensors. However, there is much space to improve the sensing performance of SMOs nanomaterials. Therefore, how to improve the sensing performance of SMOs nanomaterials for NO x gases has always attracted the interest of researchers. Up to now, there are few reviews focus on the modification strategies of SMOs which applied to NO x gas sensors. In order to compensate for the limitation, this review summarizes the existing modification strategies of SMOs, hoping to provide researchers a view of the research progress in this filed as comprehensive as possible. This review focuses on the progress of the modification of SMOs nanomaterials for chemiresistive NO x (NO, NO 2) gas sensors, including the morphology modulation of SMOs, compositing SMOs, loading noble metals, doping metal ions, compositing with carbon nanomaterials, compositing with biomass template, and compositing with MXene, MOFs, conducting polymers. The mechanism of each strategy to enhance the NO x sensing performance of SMOs-based nanomaterials is also discussed and summarized. In addition, the limitations of some of the modification strategies and ways to address them are discussed. Finally, future perspectives for SMOs-based NO x gas sensors are also discussed. [Display omitted] • An overview of modification strategies for semiconductor metal oxides applied to chemiresistive NO x gas sensors. • The mechanisms of each modification strategy to enhance gas sensing performance are summarized. • The limitations of some modification methods are mentioned and solutions to improve them are discussed. [ABSTRACT FROM AUTHOR]

The ability to quickly and accurately determine soil organic matter (SOM) content is critical for effective soil management decisions. Using the colour of soil images captured by smartphones to predict SOM content has emerged as a promising alternative to traditional wet chemistry methods. However, natural environments can present a complex array of light conditions that can compromise the accuracy and consistency of soil image colour acquisition, thus limiting the method's applicability. To address this issue, we propose five colour calibration patterns (C0 (no calibration), C1 (neutral grey), C2 (RGB), C3 (RGBCMY), and C4 (24 colours)), based on a 24-colour standard card. These patterns were used to calibrate the images of 352 original soil samples obtained from smartphones in three different lighting environments - L1 (100–2000 lx), L2 (35,000–40,000 lx), and L3 (75,000–80,000 lx). Random forest models were used to construct predictive models of soil organic matter (SOM) content based on images. Our findings indicate that smartphones exhibit complex spectral response characteristics, which result in poor image accuracy and stability of uncalibrated (C0) images under varying lighting conditions. The uncalibrated (C0) soil images in different lighting environments exhibited high colour difference (∆E mean = 14.11), resulting in poor SOM model sharing performance (R2 mean = 0.52 and RMSE mean = 20.33 g/kg). The use of colour calibration methods reduced the colour difference between soil images (∆E mean = 8.19) and improved the shared accuracy of the model (R2 mean = 0.61 and RMSE mean = 12.00 g/kg). The pattern of colour calibration has a key impact on the performance of the model application. The model sharing accuracy was found to be higher for the same or similar colour calibration pattern combination compared to different combinations of colour calibration patterns. Overall, the richer the colour calibration blocks, the better the model's shared performance. The findings of this research can enhance the application performance of soil attribute prediction models based on the colour of objects captured by smartphones in natural environments. • Differences in light intensity affect the colour stability of smartphone images. • Colour calibration reduces image colour differences in different light for phones. • Colour calibration boosts the shared ability of the attribute prediction model. • Colour calibration patterns with rich colours for better model-sharing ability. [ABSTRACT FROM AUTHOR]

Predicting stock market trends is the basic daily routine task that investors should perform in the stock trading market. Traditional market trends prediction models are generally based on hand-crafted factors or features, which heavily rely on expensive expertise knowledge. Moreover, it is difficult to discover hidden features contained in the stock time series data, which are otherwise helpful for predicting stock market trends. In this paper, we propose a novel stock market trends prediction framework SMART with a self-supervised stock technical data sequence embedding model S3E. Specifically, the model encodes stock technical data sequences into embeddings, which are further trained with multiple self-supervised auxiliary tasks. With the learned sequence embeddings, we make stock market trends predictions based on an LSTM and a feed-forward neural network. We conduct extensive experiments on China A-Shares market and NASDAQ market to show that our model is highly effective for stock market trends prediction. We further deploy SMART in a leading financial service provider in China and the result demonstrates the effectiveness of the proposed method in real-world applications. [ABSTRACT FROM AUTHOR]

Can RNA transcripts of granulosa cells be used to assess oocyte quality? The possibility of predicting the developmental competence of oocytes by RNA sequencing analysis of granulosa cells was evaluated. Granulosa cell samples were collected from 29 women undergoing assisted reproductive technology treatment and divided into two groups: 14 samples from the high blastocyst rate group and 15 from the low blastocyst rate group. Ten samples from each group were selected for RNA sequencing. A total of 129 differentially expressed genes associated with high developmental competence of oocytes were identified. COL1A2 , renin and COL1A1 were selected and further examined by quantitative real-time polymerase chain reaction (qRT-PCR). Expression levels of COL1A2 and renin by qRT-PCR were consistent with the results of RNA sequencing. RNA sequencing data could provide novel marker genes for the non-invasive evaluation of oocyte quality and embryo developmental competence. [ABSTRACT FROM AUTHOR]

This study investigated the process and mechanism of synergistically assisted hydrogen production from tailings and coal slurry. We have identified the optimal tailings as lead-zinc tailings through experimentation with different tailings mixed with pulverized coal. Furthermore, we have analyzed the electrochemical properties of Fe2+ and Fe3+ at different concentration gradients in a hydrochloric acid environment and our results have confirmed that the presence of Fe3+ in the simplified environment does not affect the electrochemical processes. to determine the respective roles and effects of the extract and residue of the lead-zinc tailings slurry with equal amounts of coal dust added to them, which has confirmed that it is the lead-zinc tailings extract that has the primary influence. In addition, the electrocatalysis of Fe3+ in the presence of both lead-zinc tailings and coal results in a small increase in current density, and the improvement in current density due to direct and indirect oxidation of coal particles is also extremely limited. Based on our analysis, we have concluded that the increase in current density brought about by the synergistic hydrogen production from lead-zinc tailings-supported coal is mainly due to the different oxidation processes of Fe2+. [Display omitted] • For the first time, tailings was used for assisted coal electrolysis to produce H 2. • Adding lead-zinc tailings improved the efficiency of coal electrolysis by 133.3%. • The reason for the significant increase in efficiency is the acceleration of the reaction by Fe2+ as an electron donor. [ABSTRACT FROM AUTHOR]

Ulcerative colitis (UC) with its rapidly increasing incidence has become an emerging challenge for public health. Therapeutic agents are required to be specifically delivered to colon epithelial cells and macrophages with controlled release in the cytoplasm for wound healing, inflammation alleviation, and microbiota rebalance. As a promising biomaterial for accomplishing this, Antheraea pernyi silk fibroin (ApSF) was selected and engineered to form nanoparticles (NPs) loaded with the anti-inflammatory drug, resveratrol (Res). The intrinsic features of these fabricated Res-ApNPs included targeting of colonic epithelial cells and macrophages, lysosomal escape capacity, and responsiveness to pH, reactive oxygen species (ROS), and glutathione, which were pertinent to their functional units such as arginine-glycine-aspartate tripeptides, α -helixes, β -sheets, and disulfide bonds, enabling on-demand release of Res molecules in the cytoplasm of target cells. The Res-ApNP treatment restored damaged colonic epithelial barriers, polarized macrophages to type M2, alleviated inflammatory reactions, and reduced the level of intracellular ROS. Oral treatment with chitosan-alginate hydrogels embedded with Res-ApNPs substantially relieved UC symptoms, as evidenced by decreased colonic inflammation, increased synthesis of tight-junction proteins, and rebalanced intestinal microbiota. Our findings suggest that these high-performance ApSF-based NPs can be developed as effective drug carriers for oral UC treatment. [Display omitted] [ABSTRACT FROM AUTHOR]