A "turn off-on" fluorescent sensor for detection of Cr(â ¥) based on upconversion nanoparticles and nanoporphyrin.

Hexavalent chromium (Cr(Ⅵ)) is a significant environmental pollutant because of its toxic and carcinogenic properties and wide use in various industries. Hence, there is an urgent need to develop accurate and selective approaches to detect the concentration of Cr(Ⅵ) in agricultural and aquaculture products to help humans avoid potential hazards of indirectly taking in Cr(Ⅵ). In this work, we report a "turn off-on" fluorescent sensor based on citric acid coated, 808 nm-excited core-shell upconversion nanoparticles (CA-UCNPs) and self-assembled copper porphyrin nanoparticles (nano CuTPyP) for sensitive and specific detection of Cr(Ⅵ). Nano copper 5, 10, 15, 20-tetra(4-pyridyl)-21H-23H- porphine obtained by acid-base neutralization micelle-confined self-assembly method function as an effective quencher due to its excellent optical property and water solubility. Through electrostatic interactions, positively charged nano CuTPyP are attracted to the surface of negatively charged CA-UCNPs, which can almost completely quench the fluorescence emission. In the presence of Cr(Ⅵ), nano CuTPyP can discriminatively interact with Cr(Ⅵ) and form nano CuTPyP/Cr(Ⅵ) complex, which separates nano CuTPyP from CA-UCNPs and restores the fluorescence. The sensing system exhibits a good linear response to Cr(Ⅵ) concentration in the range from 0.5 to 400 µM with a detection limit of 0.36 µM. The sensing method also displays high selectivity against other common ions including trivalent chromium and is applied to the analysis of Cr(Ⅵ) in actual rice and fish samples with satisfactory results.

AI-Enhanced Visual-Spectral Synergy for Fast and Ultrasensitive Biodetection of Breast Cancer-Related miRNAs.

In biomedical testing, artificial intelligence (AI)-enhanced analysis has gradually been applied to the diagnosis of certain diseases. This research employs AI algorithms to refine the precision of integrative detection, encompassing both visual results and fluorescence spectra from lateral flow assays (LFAs), which signal the presence of cancer-linked miRNAs. Specifically, the color shift of gold nanoparticles (GNPs) is paired with the red fluorescence from nitrogen vacancy color centers (NV-centers) in fluorescent nanodiamonds (FNDs) and is integrated into LFA strips. While GNPs amplify the fluorescence of FNDs, in turn, FNDs enhance the color intensity of GNPs. This reciprocal intensification of fluorescence and color can be synergistically augmented with AI algorithms, thereby improving the detection sensitivity for early diagnosis. Supported by the detection platform based on this strategy, the fastest detection results with a limit of detection (LOD) at the fM level and the R2 value of ∼0.9916 for miRNA can be obtained within 5 min. Meanwhile, by labeling the capture probes for miRNA-21 and miRNA-96 (both of which are early indicators of breast cancer) on separate T-lines, simultaneous detection of them can be achieved. The miRNA detection methods employed in this study may potentially be applied in the future for the early detection of breast cancer.

Data-Driven Battery Characterization and Prognosis: Recent Progress, Challenges, and Prospects.

Battery characterization and prognosis are essential for analyzing underlying electrochemical mechanisms and ensuring safe operation, especially with the assistance of superior data-driven artificial intelligence systems. This review provides a unique perspective on recent progress in data-driven battery characterization and prognosis methods. First, recent informative image characterization and impedance spectrum as well as high-throughput screening approaches on revealing battery electrochemical mechanisms at multiple scales are summarized. Thereafter, battery prognosis tasks and strategies are described, with the comparison of various physics-informed modeling strategies. Considering unlocking mechanisms from tremendous battery data, the dominant role of physics-informed interpretable learning in accelerating energy device development is presented. Finally, challenges and prospects on data-driven characterization and prognosis are discussed toward accelerating energy device development with much-enhanced electrochemical transparency and generalization. This review is hoped to supply new ideas and inspirations to the next-generation battery development.

Advances in Energy Harvesters/Nanogenerators and Self-Powered Sensors II.

The Internet of Things (IoT) has become a focal point in the realm of information technology and has facilitated the interconnectedness and communication of various objects, such as devices and sensors in smart cities, intelligent transportation, industrial automation, agriculture, healthcare, etc [...].

Designing and Developing a Vision-Based System to Investigate the Emotional Effects of News on Short Sleep at Noon: An Experimental Case Study.

Background: Sleep is a critical factor in maintaining good health, and its impact on various diseases has been recognized by scientists. Understanding sleep patterns and quality is crucial for investigating sleep-related disorders and their potential links to health conditions. The development of non-intrusive and contactless methods for analyzing sleep data is essential for accurate diagnosis and treatment. Methods: A novel system called the sleep visual analyzer (VSleep) was designed to analyze sleep movements and generate reports based on changes in body position angles. The system utilized camera data without requiring any physical contact with the body. A Python graphical user interface (GUI) section was developed to analyze body movements during sleep and present the data in an Excel format. To evaluate the effectiveness of the VSleep system, a case study was conducted. The participants' movements during daytime naps were recorded. The study also examined the impact of different types of news (positive, neutral, and negative) on sleep patterns. Results: The system successfully detected and recorded various angles formed by participants' bodies, providing detailed information about their sleep patterns. The results revealed distinct effects based on the news category, highlighting the potential impact of external factors on sleep quality and behaviors. Conclusions: The sleep visual analyzer (VSleep) demonstrated its efficacy in analyzing sleep-related data without the need for accessories. The VSleep system holds great potential for diagnosing and investigating sleep-related disorders. The proposed system is affordable, easy to use, portable, and a mobile application can be developed to perform the experiment and prepare the results.

How somatosensory evoked potentials improve the diagnosis of the disturbance of consciousness: A retrospective analysis.

The interpeak latency is a crucial characteristic of upper limb somatosensory evoked potentials (USEPs). However, the existing research on the correlation between interpeak latency and consciousness disorders is currently limited. We aimed to investigate how USEPs can contribute to the diagnosis of consciousness disorders. A retrospective analysis was conducted on 10 patients who underwent repetitive transcranial magnetic stimulation (rTMS) for consciousness disorders. The interpeak latency N13-N20, Glasgow coma scale (GCS), and Chinese Nanjing persistent vegetative state scale (CNPVSS) were evaluated before and after rTMS treatment, and the linear correlation between N13-N20, GCS, and CNPVSS was analysed. The scores of CNPVSS and GCS significantly increased in the first, second, and third months after rTMS. The N13-N20 was shorter in the second and third months after rTMS compared to before treatment. rTMS was found to shorten the N13-N20 latency, and there was a negative correlation between N13-N20 and the score of consciousness disorders. N13-N20 can serve as an objective index for evaluating consciousness disorders. This research provides potential insights for doctors in diagnosing patients with consciousness disorders.

High Sensitivity Electrochemical As (III) Sensor Based on Fe3O4/MoS2 Nanocomposites.

Currently, heavy metal ion pollution in water is becoming more and more common, especially As (III), which is a serious threat to human health. In this experiment, a glassy carbon electrode modified with Fe3O4/MoS2 nanocomposites was used to select the square wave voltammetry (SWV) electrochemical detection method for the detection of trace As (III) in water. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) showed that Fe3O4 nanoparticles were uniformly attached to the surface of MoS2 and were not easily agglomerated. Cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) showed that Fe3O4/MoS2 has higher sensitivity and conductivity. After optimizing the experimental conditions, the Fe3O4/MoS2-modified glassy carbon electrode exhibited high sensitivity (3.67 µA/ppb) and a low detection limit (0.70 ppb), as well as excellent interference resistance and stability for As (III).

Carbon-Based Nanomaterials Electrodes of Ionic Soft Actuators: From Initial 1D Structure to 3D Composite Structure for Flexible Intelligent Devices.

With the rapid development of autonomous and intelligent devices driven by soft actuators, ion soft actuators in flexible intelligent devices have several advantages over other actuators, including their light weight, low voltage drive, large strain, good flexibility, fast response, etc. Traditional ionic polymer metal composites have received a lot of attention over the past decades, but they suffer from poor driving performance and short service lives since the precious metal electrodes are not only expensive, heavy, and labor-intensive, but also prone to cracking with repeated actuation. As excellent candidates for the electrode materials of ionic soft actuators, carbon-based nanomaterials have received a lot of interest because of their plentiful reserves, low cost, and excellent mechanical, electrical, and electrochemical properties. This research reviewed carbon-based nanomaterial electrodes of ion soft actuators for flexible smart devices from a fresh perspective from 1D to 3D combinations. The design of the electrode structure is introduced after the driving mechanism of ionic soft actuators. The details of ionic soft actuator electrodes made of carbon-based nanomaterials are then provided. Additionally, a summary of applications for flexible intelligent devices is provided. Finally, suggestions for challenges and prospects are made to offer direction and inspiration for further development.

Triboelectric-induced ion mobility for artificial intelligence-enhanced mid-infrared gas spectroscopy.

Isopropyl alcohol molecules, as a biomarker for anti-virus diagnosis, play a significant role in the area of environmental safety and healthcare relating volatile organic compounds. However, conventional gas molecule detection exhibits dramatic drawbacks, like the strict working conditions of ion mobility methodology and weak light-matter interaction of mid-infrared spectroscopy, yielding limited response of targeted molecules. We propose a synergistic methodology of artificial intelligence-enhanced ion mobility and mid-infrared spectroscopy, leveraging the complementary features from the sensing signal in different dimensions to reach superior accuracy for isopropyl alcohol identification. We pull in "cold" plasma discharge from triboelectric generator which improves the mid-infrared spectroscopic response of isopropyl alcohol with good regression prediction. Moreover, this synergistic methodology achieves ~99.08% accuracy for a precise gas concentration prediction, even with interferences of different carbon-based gases. The synergistic methodology of artificial intelligence-enhanced system creates mechanism of accurate gas sensing for mixture and regression prediction in healthcare.

Rosuvastatin suppresses TNF-α-induced matrix catabolism, pyroptosis and senescence via the HMGB1/NF-κB signaling pathway in nucleus pulposus cells.

Intervertebral disc degeneration is mainly caused by irregular matrix metabolism in nucleus pulposus cells and involves inflammatory factors such as TNF-α. Rosuvastatin, which is widely used in the clinic to reduce cholesterol levels, exerts anti-inflammatory effects, but whether rosuvastatin participates in IDD remains unclear. The current study aims to investigate the regulatory effect of rosuvastatin on IDD and the potential mechanism. In vitro experiments demonstrate that rosuvastatin promotes matrix anabolism and suppresses catabolism in response to TNF-α stimulation. In addition, rosuvastatin inhibits cell pyroptosis and senescence induced by TNF-α. These results demonstrate the therapeutic effect of rosuvastatin on IDD. We further find that HMGB1, a gene closely related to cholesterol metabolism and the inflammatory response, is upregulated in response to TNF-α stimulation. HMGB1 inhibition or knockdown successfully alleviates TNF-α-induced ECM degradation, senescence and pyroptosis. Subsequently, we find that HMGB1 is regulated by rosuvastatin and that its overexpression abrogates the protective effect of rosuvastatin. We then verify that the NF-κB pathway is the underlying pathway regulated by rosuvastatin and HMGB1. In vivo experiments also reveal that rosuvastatin inhibits IDD progression by alleviating pyroptosis and senescence and downregulating HMGB1 and p65. This study might provide new insight into therapeutic strategies for IDD.

Effect of Different Fine Aggregate Characteristics on Fracture Toughness and Microstructure of Sand Concrete.

The fracture toughness of sand concrete is affected by aggregate characteristics. In order to study the possibility of exploiting tailings sand, available in large quantities in sand concrete, and find an approach to improve the toughness of sand concrete by selecting appropriate fine aggregate. Three distinct fine aggregates have been used. After characterizing the fine aggregate used, the mechanical properties were tested to characterize the toughness of sand concrete, the box-counting fractal dimensions were calculated to analyze the roughness of fracture surfaces, and the microstructure was tested to observe the path and width of microcracks and hydration products in sand concrete. The results show that the mineral composition of fine aggregates is close, but their fineness modulus, fine aggregate angularity (FAA) and gradation vary considerably; FAA has a significant impact on the fracture toughness of sand concrete. The higher the FAA value, the more resistant it is to crack expansion; with the FAA values of from 32 s to 44 s, the microcrack width in sand concrete was reduced from 0.25 um to 0.14 um; The fracture toughness and microstructure of sand concrete are also related to the gradation of fine aggregates, the better gradation can improve the performance of the interfacial transition zone (ITZ). The hydration products in the ITZ are also different because more reasonable gradation of aggregates reduces the voids between the fine aggregates and the cement paste and restrains the full growth of crystals. These results demonstrate that sand concrete has promising applications in the field of construction engineering.

BRD9 Inhibition Attenuates Matrix Degradation and Pyroptosis in Nucleus Pulposus by Modulating the NOX1/ROS/NF-κB axis.

Intervertebral disc degeneration (IDD) is considered to be the leading cause of low back pain (LBP). The progression of IDD is closely related to the inflammatory microenvironment, which results in extracellular matrix degradation and cell death. One of the proteins, which have been shown to participate in the inflammatory response, is the bromodomain-containing protein 9 (BRD9). This study aimed to investigate the role and mechanism of BRD9 in regulating IDD. The tumor necrosis factor-α (TNF-α) was used to mimic the inflammatory microenvironment in vitro. Western blot, RT-PCR, immunohistochemistry, immunofluorescence, and flow cytometry were used to demonstrate the effect of BRD9 inhibition or knockdown on matrix metabolism and pyroptosis. We found that the expression of BRD9 was upregulated as IDD progressed. BRD9 inhibition or knockdown alleviated TNF-α-induced matrix degradation, reactive oxygen species (ROS) production, and pyroptosis in rat nucleus pulposus cells. Mechanistically, RNA-seq was used to investigate the mechanism of BRD9 in promoting IDD. Further investigation revealed that BRD9 regulated NOX1 expression. Inhibition of NOX1 could abrogate matrix degradation, ROS production, and pyroptosis caused by BRD9 overexpression. In vivo, the radiological and histological evaluation showed that the pharmacological inhibition of BRD9 alleviated IDD development in rat IDD model. Our results indicated that BRD9 could promote IDD via the NOX1/ROS/ NF-κB axis by inducing matrix degradation and pyroptosis. Targeting BRD9 may be a potential therapeutic strategy in treating IDD.

Biomimetic human eyes in adaptive lenses with conductive gels.

To investigate the imaging effect, adaptive robust lenses are prepared by sealing transparent liquid or gel. Lenses are fabricated using the negative-pressure method, which is a benefit for a stable biconvex shape. Under the action of an electric field, the soft lens deforms following the dielectric elastomer actuator (DEA). DE (dielectric elastomer) membranes expand in the plane perpendicular to the electric field lines. The toroidal driving area leads to a decrease in lens diameter and an increase in convex curvature. Therefore, the focal length of the lens becomes shorter. The experimental measurement utilizes the double focal length method. As a result, the largest focal length change that could be achieved was 44.7% (190 mm→105 mm) of the soft lens using a DEA with carbon grease electrodes. Furthermore, the ECG (electrocardiogram) conductive gel could replace traditional carbon grease for DEA electrodes in optics. This type of transparent electrode is creatively applied to a biomedical lens. Under the same conditions, the electrostriction rate in a DEA with ECG gel was achieved at 33%, which was greater than that of 28% in a DEA coupled with carbon grease electrode. Adaptive lenses have characteristics such as easy fabrication, low cost, and strong operability, and they possess great potential application value in biomedical feild.

Special Issue: Advance in Energy Harvesters/Nanogenerators and Self-Powered Sensors.

Internet of things (IoT) technologies are greatly promoted by the rapidly developed 5G-and-beyond networks, which have spawned diversified applications in the new era including smart homes, digital health, sports training, robotics, human-machine interaction, metaverse, smart manufacturing and industry 4 [...].

Self-Attention-Augmented Generative Adversarial Networks for Data-Driven Modeling of Nanoscale Coating Manufacturing.

Nanoscale coating manufacturing (NCM) process modeling is an important way to monitor and modulate coating quality. The multivariable prediction of coated film and the data augmentation of the NCM process are two common issues in smart factories. However, there has not been an artificial intelligence model to solve these two problems simultaneously. Focusing on the two problems, a novel auxiliary regression using a self-attention-augmented generative adversarial network (AR-SAGAN) is proposed in this paper. This model deals with the problem of NCM process modeling with three steps. First, the AR-SAGAN structure was established and composed of a generator, feature extractor, discriminator, and regressor. Second, the nanoscale coating quality was estimated by putting online control parameters into the feature extractor and regressor. Third, the control parameters in the recipes were generated using preset parameters and target quality. Finally, the proposed method was verified by the experiments of a solar cell antireflection coating dataset, the results of which showed that our method performs excellently for both multivariable quality prediction and data augmentation. The mean squared error of the predicted thickness was about 1.6~2.1 nm, which is lower than other traditional methods.

Advances in Electrochemical Detection Electrodes for As(III).

Arsenic is extremely abundant in the Earth's crust and is one of the most common environmental pollutants in nature. In the natural water environment and surface soil, arsenic exists mainly in the form of trivalent arsenite (As(III)) and pentavalent arsenate (As(V)) ions, and its toxicity can be a serious threat to human health. In order to manage the increasingly serious arsenic pollution in the living environment and maintain a healthy and beautiful ecosystem for human beings, it is urgent to conduct research on an efficient sensing method suitable for the detection of As(III) ions. Electrochemical sensing has the advantages of simple instrumentation, high sensitivity, good selectivity, portability, and the ability to be analyzed on site. This paper reviews various electrode systems developed in recent years based on nanomaterials such as noble metals, bimetals, other metals and their compounds, carbon nano, and biomolecules, with a focus on electrodes modified with noble metal and metal compound nanomaterials, and evaluates their performance for the detection of arsenic. They have great potential for achieving the rapid detection of arsenic due to their excellent sensitivity and strong interference immunity. In addition, this paper discusses the relatively rare application of silicon and its compounds as well as novel polymers in achieving arsenic detection, which provides new ideas for investigating novel nanomaterial sensing. We hope that this review will further advance the research progress of high-performance arsenic sensors based on novel nanomaterials.

Artificial Intelligence of Manufacturing Robotics Health Monitoring System by Semantic Modeling.

Robotics is widely used in nearly all sorts of manufacturing. Steady performance and accurate movement of robotics are vital in quality control. Along with the coming of the Industry 4.0 era, oceans of sensor data from robotics are available, within which the health condition and faults are enclosed. Considering the growing complexity of the manufacturing system, an automatic and intelligent health-monitoring system is required to detect abnormalities of robotics in real-time to promote quality and reduce safety risks. Therefore, in this study, we designed a novel semantic-based modeling method for multistage robotic systems. Experiments show that sole modeling is not sufficient for multiple stages. We propose a descriptor to conclude the stages of robotic systems by learning from operational data. The descriptors are akin to a vocabulary of the systems; hence, semantic checking can be carried out to monitor the correctness of operations. Furthermore, the stage classification and its semantics were used to apply various regression models to each stage to monitor the quality of each operation. The proposed method was applied to a photovoltaic manufacturing system. Benchmarks on production datasets from actual factories show the effectiveness of the proposed method to realize an AI-enabled real-time health-monitoring system of robotics.

Spectrum Analysis Enabled Periodic Feature Reconstruction Based Automatic Defect Detection System for Electroluminescence Images of Photovoltaic Modules.

Electroluminescence (EL) imaging is a widely adopted method in quality assurance of the photovoltaic (PV) manufacturing industry. With the growing demand for high-quality PV products, automatic inspection methods based on machine vision have become an emerging area concern to replace manual inspectors. Therefore, this paper presents an automatic defect-inspection method for multi-cell monocrystalline PV modules with EL images. A processing routine is designed to extract the defect features of the PV module, eliminating the influence of the intrinsic structural features. Spectrum domain analysis is applied to effectively reconstruct an improved PV layout from a defective one by spectrum filtering in a certain direction. The reconstructed image is used to segment the PV module into cells and slices. Based on the segmentation, defect detection is carried out on individual cells or slices to detect cracks, breaks, and speckles. Robust performance has been achieved from experiments on many samples with varying illumination conditions and defect shapes/sizes, which shows the proposed method can efficiently distinguish intrinsic structural features from the defect features, enabling precise and speedy defect detections on multi-cell PV modules.

Atorvastatin inhibited TNF-α induced matrix degradation in rat nucleus pulposus cells by suppressing NLRP3 inflammasome activity and inducing autophagy through NF-κB signaling.

Intervertebral disc degeneration (IDD) is one of the main causes of lower back pain (LBP). It results from an imbalance between the degradation and synthesis of extracellular matrix (ECM) components in nucleus pulposus (NP) cells. Atorvastatin, an HMG-CoA reductase inhibitor, plays a vital role in many diseases, such as cardiovascular disease and osteoarthritis. However, the effect of atorvastatin on IDD is unclear. Herein, we demonstrated that atorvastatin affects matrix degradation induced by TNF-α and demonstrated the mechanism by which TNF-α modulates matrix metabolism in rat NP cells. Real-time PCR, western blotting and immunofluorescence staining were performed to detect the mRNA and protein expression of related genes. mRFP-GFP-LC3 adenovirus plasmid transfection and transmission electron microscopy (TEM) were used to detect cell autophagy. NLRP3 inhibitor and lentiviral vectors containing shRNA-NLRP3 were used to show the effect of NLRP3 on autophagic flux and the NF-κB signaling pathway. The results revealed that atorvastatin might suppress matrix degradation induced by TNF-α by suppressing NLRP3 inflammasome activity and inducing autophagic flux. Moreover, atorvastatin suppressed NF-κB signaling induced by TNF-α. NF-κB signaling inhibition suppressed NLRP3 inflammasome activity, and NLRP3 inhibition suppressed NF-κB signaling activation induced by TNF-α. NLRP3 inhibition or NLRP3 knockdown induced autophagic flux in the presence of TNF-α. Overall, the present study demonstrated that atorvastatin might suppress matrix degradation induced by TNF-α and further revealed the crosstalk among NLRP3 inflammasome activity, autophagy and NF-κB signaling.

Volatile organic compounds sensing based on Bennet doubler-inspired triboelectric nanogenerator and machine learning-assisted ion mobility analysis.

Ion mobility analysis is a well-known analytical technique for identifying gas-phase compounds in fast-response gas-monitoring systems. However, the conventional plasma discharge system is bulky, operates at a high temperature, and inappropriate for volatile organic compounds (VOCs) concentration detection. Therefore, we report a machine learning (ML)-enhanced ion mobility analyzer with a triboelectric-based ionizer, which offers good ion mobility selectivity and VOC recognition ability with a small-sized device and non-strict operating environment. Based on the charge accumulation mechanism, a multi-switched manipulation triboelectric nanogenerator (SM-TENG) can provide a direct current (DC) bias at the order of a few hundred, which can be further leveraged as the power source to obtain a unique and repeatable discharge characteristic of different VOCs, and their mixtures, with a special tip-plate electrode configuration. Aiming to tackle the grand challenge in the detection of multiple VOCs, the ML-enhanced ion mobility analysis method was successfully demonstrated by extracting specific features automatically from ion mobility spectrometry data with ML algorithms, which significantly enhance the detection ability of the SM-TENG based VOC analyzer, showing a portable real-time VOC monitoring solution with rapid response and low power consumption for future internet of things based environmental monitoring applications.