Renewable symmetric supercapacitors assembled with lignin nanoparticles-based thin film electrolyte and carbon aerogel electrodes.

Lignin as a natural biopolymer is becoming increasingly in demand due to its eco-friendly properties, while lignin-based electrolyte with high conductivity and reliable durability for applications in supercapacitors is still challenging. Herein, a facile method to prepare lignin nanoparticles (LNPs)-based solid electrolyte thin film (LF) was proposed through chemical cross-linking reaction. The fabricated LF exhibited a distinctive spongy porous structure with the ionic conductivity of 3.26 mS cm-1, demonstrating the exceptional flexibility and favorable mechanical properties. Moreover, the assembly of all-LNPs-based symmetric supercapacitor (SSC) devices was achieved using LF electrolyte and LCA electrodes for the first time, confirming the LF3 electrolyte superior to commercial cellulose separator in capacitive behaviour. This SSC device exhibited a specific capacitance of 122.7 F g-1 at 0.5 A g-1 and the maximum energy density of 17.04 W h kg-1. Furthermore, the incorporation of sodium alginate (SA) significantly enhanced the ionic conductivity of SA/LF3 electrolyte, and the resulting SSC device delivered a higher specific capacitance of 174.5 F g-1 at 0.5 A g-1 and the maximum energy and power densities of 24.24 W h kg-1 and 5023 W kg-1, respectively. This study proposes a promising approach for sustainable utilization of lignin in energy storage applications.

Harnessing a silicon carbide nanowire photoelectric synaptic device for novel visual adaptation spiking neural networks.

Visual adaptation is essential for optimizing the image quality and sensitivity of artificial vision systems in real-world lighting conditions. However, additional modules, leading to time delays and potentially increasing power consumption, are needed for traditional artificial vision systems to implement visual adaptation. Here, an ITO/PMMA/SiC-NWs/ITO photoelectric synaptic device is developed for compact artificial vision systems with the visual adaption function. The theoretical calculation and experimental results demonstrated that the heating effect, induced by the increment light intensity, leads to the photoelectric synaptic device enabling the visual adaption function. Additionally, a visual adaptation artificial neuron (VAAN) circuit was implemented by incorporating the photoelectric synaptic device into a LIF neuron circuit. The output frequency of this VAAN circuit initially increases and then decreases with gradual light intensification, reflecting the dynamic process of visual adaptation. Furthermore, a visual adaptation spiking neural network (VASNN) was constructed to evaluate the photoelectric synaptic device based visual system for perception tasks. The results indicate that, in the task of traffic sign detection under extreme weather conditions, an accuracy of 97% was achieved (which is approximately 12% higher than that without a visual adaptation function). Our research provides a biologically plausible hardware solution for visual adaptation in neuromorphic computing.

Screening Tool for Paroxysmal Atrial Fibrillation Based on a Deep-Learning Algorithm Using Printed 12-Lead Electrocardiographic Records during Sinus Rhythm.

Background: Recent advancements in artificial intelligence (AI) have significantly improved atrial fibrillation (AF) detection using electrocardiography (ECG) data obtained during sinus rhythm (SR). However, the utility of printed ECG (pECG) records for AF detection, particularly in developing countries, remains unexplored. This study aims to assess the efficacy of an AI-based screening tool for paroxysmal AF (PAF) using pECGs during SR. Methods: We analyzed 5688 printed 12-lead SR-ECG records from 2192 patients admitted to Beijing Chaoyang Hospital between May 2011 to August 2022. All patients underwent catheter ablation for PAF (AF group) or other electrophysiological procedures (non-AF group). We developed a deep learning model to detect PAF from these printed SR-ECGs. The 2192 patients were randomly assigned to training (1972, 57.3% with PAF), validation (108, 57.4% with PAF), and test datasets (112, 57.1% with PAF). We developed an applet to digitize the printed ECG data and display the results within a few seconds. Our evaluation focused on sensitivity, specificity, accuracy, F1 score, the area under the receiver-operating characteristic curve (AUROC), and precision-recall curves (PRAUC). Results: The PAF detection algorithm demonstrated strong performance: sensitivity 87.5%, specificity 66.7%, accuracy 78.6%, F1 score 0.824, AUROC 0.871 and PRAUC 0.914. A gradient-weighted class activation map (Grad-CAM) revealed the model's tailored focus on different ECG areas for personalized PAF detection. Conclusions: The deep-learning analysis of printed SR-ECG records shows high accuracy in PAF detection, suggesting its potential as a reliable screening tool in real-world clinical practice.

Highly Promising 2D/1D BP-C/CNT Bionic Opto-Olfactory Co-Sensory Artificial Synapses for Multisensory Integration.

The development of high-performance artificial synaptic neuromorphic devices poses a significant challenge in the creation of biomimetic sensing neural systems that seamlessly integrate both sensory and computational functionalities. In pursuit of this objective, promising bionic opto-olfactory co-sensory artificial synapse devices are constructed utilizing the BP-C/CNT (2D/1D) hybrid filter membrane as the resistive layer. Experimental results demonstrated that the devices seamlessly integrated the light modulation, gas detection, and biological synaptic functions into a single device while addressing the challenge with separating artificial synaptic devices from sensors. These devices offered the following advantages: 1) Simulating visual synapses, they can effectively replicate fundamental synaptic functions under both electrical and optical stimulation. 2) By emulating olfactory synapse responses to specific gases, they can achieve ultra-low detection limits and rapid identification of ethanol and acetone gases. 3) They enable photo-olfactory co-sensing simulations that mimic synaptic function under light-modulated pulse conditions in distinct gas environments, facilitating the study of synaptic learning rules and Pavlovian responses. This work provides a pioneering approach for exploring highly stable 2D BP-based optoelectronics and advancing the development of biomimetic neural systems.

SiC@NiO Core-Shell Nanowire Networks-Based Optoelectronic Synapses for Neuromorphic Computing and Visual Systems at High Temperature.

1D nanowire networks, sharing similarities of structure, information transfer, and computation with biological neural networks, have emerged as a promising platform for neuromorphic systems. Based on brain-like structures of 1D nanowire networks, neuromorphic synaptic devices can overcome the von Neumann bottleneck, achieving intelligent high-efficient sensing and computing function with high information processing rates and low power consumption. Here, high-temperature neuromorphic synaptic devices based on SiC@NiO core-shell nanowire networks optoelectronic memristors (NNOMs) are developed. Experimental results demonstrate that NNOMs attain synaptic short/long-term plasticity and modulation plasticity under both electrical and optical stimulation, and exhibit advanced functions such as short/long-term memory and "learning-forgetting-relearning" under optical stimulation at both room temperature and 200 °C. Based on the advanced functions under light stimulus, the constructed 5 × 3 optoelectronic synaptic array devices exhibit a stable visual memory function up to 200 °C, which can be utilized to develop artificial visual systems. Additionally, when exposed to multiple electronic or optical stimuli, the NNOMs effectively replicate the principles of Pavlovian classical conditioning, achieving visual heterologous synaptic functionality and refining neural networks. Overall, with abundant synaptic characteristics and high-temperature thermal stability, these neuromorphic synaptic devices offer a promising route for advancing neuromorphic computing and visual systems.

Artificial Optoelectronic Synapse Based on Violet Phosphorus Microfiber Arrays.

Memristor-based artificial synapses are regarded as the most promising candidate to develop brain-like neuromorphic network computers and overcome the bottleneck of Von-Neumann architecture. Violet phosphorus (VP) as a new allotrope of available phosphorus with outstanding electro-optical properties and stability has attracted more and more attention in the past several years. In this study, large-scale, high-yield VP microfiber vertical arrays have been successfully developed on a Sn-coated graphite paper and are used as the memristor functional layers to build reliable, low-power artificial synaptic devices. The VP devices can well mimic the major synaptic functions such as short-term memory (STM), long-term memory (LTM), paired-pulse facilitation (PPF), spike timing-dependent plasticity (STDP), and spike rate-dependent plasticity (SRDP) under both electrical and light stimulation conditions, even the dendritic synapse functions and simple logical operations. By virtue of the excellent performance, the VP artificial synapse devices can be conductive to building high-performance optic-neural synaptic devices simulating the human-like optic nerve system. On this basis, Pavlov's associative memory can be successfully implemented optically. This study provides a promising approach for the design and manufacture of VP-based artificial synaptic devices and outlines a direction with multifunctional neural devices.

TiO2 nanotube arrays-based photoelectrocatalyst: Tri-Doping engineering and carbon coating engineering boosting visible activity, and stable hydrogen evolution.

The integration of non-metallic doping and carbon coating for TiO2-based photoelectrocatalysts can be recognized as a promising strategy to enhance their hydrogen production performance. To this end, this study explored the carbon coating engineering to induce stable multi-element doping with an aim to develop high-performance TiO2 nanotube array-based photoelectrocatalysts. The resulting structures consisted of carbon-nitrogen-sulfur-tri-doped TiO2 nanotube arrays with a nitrogen-sulfur-codoped carbon coating (CNS-TNTA/NSC). The fabrication process involved a one-step, low-cost strategy of the carbon-coated tridoped reaction confined in vacuum space, utilizing polymer thiourea sealed in a controlled environment. Compared the photocurrent density of CNS-TNTA/NSC with pristine TNTA, the photocurrent enhancement of approximately 18.3-fold under simulated sunlight and a remarkable increase of 32.8-fold under simulated visible light conditions. The enhanced photocatalytic activity under visible light was ascribed to two factors: First, C, N, and S tri-doping and Ti3+ created a diverse array of impurity energy levels within the band gap, which synergistically narrowed the band gap and further enhanced response to the visible light range. Second, the presence of a carbon coating shell doped with N and S can greatly promote electron transfer and efficient electron-hole pair separation. This study could provide significant insights concerning the design of sophisticated photoanodes.

Meroterpenoids from Daphne genkwa shows promising in vitro antitumor activity via inhibiting PI3K/Akt/mTOR signaling pathway in A549 cells.

Phytochemical investigation into the leaves and branches of Daphne genkwa afforded 25 meroterpenoids (1-16) including nine pairs of enantiomers (1a/1b-8a/8b and 12a/12b), among which 20 compounds have been reported in the present work for the first time. The structures with absolute configurations of the new molecules (excluding 10-13) were established via comprehensive spectroscopic analyses especially electronic circular dichroism (ECD) and Mosher's methods. A preliminary in vitro cell viability assay revealed remarkable cytotoxicities of selective compounds against A549 (lung), Hela (cervical), MDA-MB231 (breast) and MCF-7 (breast) cancer cells, and compound 8a showed the best inhibitory activity with IC50 values in the range of 3.12-4.67 µM toward the four cell lines. Subsequent in vitro antitumor evaluation of 8a disclosed that it could inhibit the proliferation and metastasis, as well as induce significant apoptosis and cycle arrest, of A549 cells. Further mechanistic investigations revealed that 8a could exert its antitumor activity via inhibiting the PI3K/Akt/mTOR signaling pathway.

Ultralow Power and Shifting-Discretized Magnetic Racetrack Memory Device Driven by Chirality Switching and Spin Current.

Magnetic racetrack memory has significantly evolved and developed since its first experimental verification and is considered one of the most promising candidates for future high-density on-chip solid-state memory. However, both the lack of a fast and precise magnetic domain wall (DW) shifting mechanism and the required extremely high DW motion (DWM) driving current make the racetrack difficult to commercialize. Here, we propose a method for coherent DWM that is free from the above issues, which is driven by chirality switching (CS) and an ultralow spin-orbit-torque (SOT) current. The CS, as the driving force of DWM, is achieved by the sign change of the Dzyaloshinskii-Moriya interaction, which is further induced by a ferroelectric switching voltage. The SOT is used to break the symmetry when the magnetic moment is rotated in the Bloch direction. We numerically investigate the underlying principle and the effect of key parameters on the DWM by micromagnetic simulations. Under the CS mechanism, a fast (∼102 m/s), ultralow energy (∼5 attoJoule), and precisely discretized DWM can be achieved. Considering that skyrmions with topological protection and smaller size are also promising for future racetracks, we similarly evaluate the feasibility of applying such a CS mechanism to a skyrmion. However, we find that the CS causes it to "breathe" instead of moving. Our results demonstrate that the CS strategy is suitable for future DW racetrack memory with ultralow power consumption and discretized DWM.

Dual-functional Network Regulation Underlies the Central Executive System in Working Memory.

The frontoparietal network (FPN) and cingulo-opercular network (CON) may exert top-down regulation corresponding to the central executive system (CES) in working memory (WM); however, contributions and regulatory mechanisms remain unclear. We examined network interaction mechanisms underpinning the CES by depicting CON- and FPN-mediated whole-brain information flow in WM. We used datasets from participants performing verbal and spatial working memory tasks, divided into encoding, maintenance, and probe stages. We used general linear models to obtain task-activated CON and FPN nodes to define regions of interest (ROI); an online meta-analysis defined alternative ROIs for validation. We calculated whole-brain functional connectivity (FC) maps seeded by CON and FPN nodes at each stage using beta sequence analysis. We used Granger causality analysis to obtain the connectivity maps and assess task-level information flow patterns. For verbal working memory, the CON functionally connected positively and negatively to task-dependent and task-independent networks, respectively, at all stages. FPN FC patterns were similar only in the encoding and maintenance stages. The CON elicited stronger task-level outputs. Main effects were: stable CON â†’ FPN, CON â†’ DMN, CON â†’ visual areas, FPN â†’ visual areas, and phonological areas â†’ FPN. The CON and FPN both up-regulated task-dependent and down-regulated task-independent networks during encoding and probing. Task-level output was slightly stronger for the CON. CON â†’ FPN, CON â†’ DMN, visual areas â†’ CON, and visual areas â†’ FPN showed consistent effects. The CON and FPN might together underlie the CES's neural basis and achieve top-down regulation through information interaction with other large-scale functional networks, and the CON may be a higher-level regulatory core in WM.

Nonmetallic SERS-based biosensor for ultrasensitive and reproducible immunoassay of ferritin mediated by magnetic molybdenum disulfide nanoflowers and black phosphorus nanosheets.

To improve the curability of cancer patients, it is essential to propose an early diagnosis technology with ultra-high sensitivity and reliable biocompatibility. Herein, a sophisticated nonmetallic SERS-based immunosensor, comprised by a MoS2 @Fe3O4 nanoflower-based immunoprobe with magnetism and a black phosphorus (BP) nanosheet-based immunosubstrate, was proposed for the specific in-situ monitoring of ferritin (FER). The sandwich immunosensor was endowed with an excellent SERS performance mainly ascribed to a synergistic chemical enhancement as well as an additional electrostatic adsorption effect, achieving a limit of detection down to 7.3 × 10-5 µg/mL. Particularly, all the Raman label, target FER, and anti-FER could be completely degraded within 70 min under visible light irradiation owing to the favorable photocatalytic activities of MoS2 and BP which could be then effectively separated and collected with the assistance of an external magnet. Such a recyclable nonmetallic immunosensor holds great potential and practicality in the clinical screening of cancer.

Exploring the Lifelong Changes of Interaction between Cingulo-Opercular Network and Other Cognitive Control Related Functional Networks Based on Multiple Connectivity Indices.

BACKGROUND: The cingulo-opercular network (CON) has been proposed to play a central role in cognitive control. The lifetime change mechanism of its integrity and interaction with other cognitive control-related functional networks (CCRNs) is closely associated with developing cognitive control behaviors but needs further elucidation. METHODS: The resting-state functional magnetic resonance imaging data were recorded from 207 subjects, who were divided into three age groups: age 4-20, 21-59, and 60-85 years old. For each group, multiple indices (cross-correlation, total independence, and Granger causality) within CON and between CON and other cognitive control-related functional networks (dorsal attention network, DAN; central executive network, CEN; default mode network, DMN) were calculated and correlated with age to yield maps that delineated the changing pattern of CON-related interaction. RESULTS: We found three main results. (1) The connectivity indices within the CON and between CON and the other three CCRNs showed significant enhancement from childhood to early adulthood (age 4-20 years), (2) mild attenuation within CON from early adulthood to middle age (age 21-59 years), and (3) significant attenuation within CON and between CON and DMN in the elder group (age 60-85 years). CONCLUSIONS: The results indicated the prominently increased integrity of within-CON and CON-CCRNs communication, mildly weakened within-CON communication, and significantly attenuated within-CON and CON-DMN communication, characterizing distinct changing patterns of CON-interaction at three different stages that covered a life-long span.

A non-metallic SERS-based immunoassay founded by light-harvesting effect and strengthened chemical enhancement.

Owing to its promising biocompatibility and reliable sensitivity, semiconductor-guided surface-enhanced Raman scattering (SERS) technology has aroused widespread concern in clinical immunoassays. Herein, the well-improved light capture capability of MoS2 with a novel three-dimensional (3D) flower-like morphology was combined with the synergistic chemical enhancement from a MoS2@red phosphorus (RP) hybrid system, facilitating an attractive non-metallic SERS-based detection of ferritin in serum. Owing to the remarkable enhancement factors of both the immunoprobe and immunosubstrate, which were comparable to noble metal, an extremely low limit detection of 11.5 pg mL-1 was achieved in the absence of fluorescence interference. In particular, the trace ferritin in the clinical serum sample was successfully monitored, demonstrating superior sensitivity to the traditional chemiluminescent method. Overall, this study convincingly revealed the feasibility and reliability of SERS-based immunoassays induced by biocompatible semiconductors, which has opened a new way to implement the detection and tracking of biomarkers in the human body.

Unbalanced amygdala communication in major depressive disorder.

BACKGROUND: Previous studies suggested an association between functional alteration of the amygdala and typical major depressive disorder (MDD) symptoms. Examining whether and how the interaction between the amygdala and regions/functional networks is altered in patients with MDD is important for understanding its neural basis. METHODS: Resting-state functional magnetic resonance imaging data were recorded from 67 patients with MDD and 74 age- and sex-matched healthy controls (HCs). A framework for large-scale network analysis based on seed mappings of amygdala sub-regions, using a multi-connectivity-indicator strategy (cross-correlation, total interdependencies (TI), Granger causality (GC), and machine learning), was employed. Multiple indicators were compared between the two groups. The altered indicators were ranked in a supporting-vector machine-based procedure and associated with the Hamilton Rating Scale for Depression scores. RESULTS: The amygdala connectivity with the default mode network and ventral attention network regions was enhanced and that with the somatomotor network, dorsal frontoparietal network, and putamen regions in patients with MDD was reduced. The machine learning analysis highlighted altered indicators that were most conducive to the classification between the two groups. LIMITATIONS: Most patients with MDD received different pharmacological treatments. It is difficult to illustrate the medication state's effect on the alteration model because of its complex situation. CONCLUSION: The results indicate an unbalanced interaction model between the amygdala and functional networks and regions essential for various emotional and cognitive functions. The model can help explain potential aberrancy in the neural mechanisms that underlie the functional impairments observed across various domains in patients with MDD.

Large tunneling magnetoresistance in van der Waals magnetic tunnel junctions based on FeCl2 films with interlayer antiferromagnetic couplings.

Antiferromagnets (AFMs) are some of the most promising candidates for next-generation magnetic memory technology owing to their advantages over conventional ferromagnets (FMs), such as zero stray field and THz-range magnetic resonance frequency. Motivated by the recent synthesis of FeCl2 films with interlayer AFM and intralayer FM couplings, we investigated the magnetic properties of few-layer FeCl2 and the spin-dependent transmissions of graphite/bilayer FeCl2/graphite and Au/n-layer FeCl2/Au magnetic tunnel junctions (MTJs) using first-principles calculations combined with the nonequilibrium Green's function. The interlayer AFM coupling of FeCl2 is certified to be stable and independent of the stacking orders and relative displacement between layers. Furthermore, based on the Au electrode with better conductive performance than the graphite electrode and monolayer 1T-FeCl2 with complete spin polarization, high Curie temperature and large magnetic anisotropic energy, a high tunnel magnetoresistance (TMR) ratio of 2.7 × 103% is achieved in Au/bilayer FeCl2/Au MTJs at zero bias and it increases with different layers of FeCl2 (n = 2-10). These excellent spin transport properties of Au/n-layer FeCl2/Au MTJs based on two-dimensional (2D) AFM barriers with out-of-plane magnetization directions suggest their great potential for application in high-reliability, high-speed and high-density spintronic devices.

Estimating critical values from electrocardiogram using a deep ordinal convolutional neural network.

BACKGROUND: Critical values are commonly used in clinical laboratory tests to define health-related conditions of varying degrees. Knowing the values, people can quickly become aware of health risks, and the health professionals can take immediate actions and save lives. METHODS: In this paper, we propose a method that extends the concept of critical value to one of the most commonly used physiological signals in the clinical environment-Electrocardiogram (ECG). We first construct a mapping from common ECG diagnostic conclusions to critical values. After that, we build a 61-layer deep convolutional neural network named CardioV, which is characterized by an ordinal classifier. RESULTS: We conduct experiments on a large public ECG dataset, and demonstrate that CardioV achieves a mean absolute error of 0.4984 and a ROC-AUC score of 0.8735. In addition, we find that the model performs better for extreme critical values and the younger age group, while gender does not affect the performance. The ablation study confirms that the ordinal classification mechanism suits for estimating the critical values which contain ranking information. Moreover, model interpretation techniques help us discover that CardioV focuses on the characteristic ECG locations during the critical value estimation process. CONCLUSIONS: As an ordinal classifier, CardioV performs well in estimating ECG critical values that can help people quickly identify different heart conditions. We obtain ROC-AUC scores above 0.8 for all four critical value categories, and find that the extreme values (0 (no risk) and 3 (high risk)) have better model performance than the other two (1 (low risk) and 2 (medium risk)). Results also show that gender does not affect the performance, and the older age group has worse performance than the younger age group. In addition, visualization techniques reveal that the model pays more attention to characteristic ECG locations.

Tooth Alignment Network Based on Landmark Constraints and Hierarchical Graph Structure.

Automatic tooth alignment target prediction is vital in shortening the planning time of orthodontic treatments and aligner designs. Generally, the quality of alignment targets greatly depends on the experience and ability of dentists and has enormous subjective factors. Therefore, many knowledge-driven alignment prediction methods have been proposed to help inexperienced dentists. Unfortunately, existing methods tend to directly regress tooth motion, which lacks clinical interpretability. Tooth anatomical landmarks play a critical role in orthodontics because they are effective in aiding the assessment of whether teeth are in close arrangement and normal occlusion. Thus, we consider anatomical landmark constraints to improve tooth alignment results. In this paper, we present a novel tooth alignment neural network for alignment target predictions based on tooth landmark constraints and a hierarchical graph structure. We detect the landmarks of each tooth first and then construct a hierarchical graph of jaw-tooth-landmark to characterize the relationship between teeth and landmarks. Then, we define the landmark constraints to guide the network to learn the normal occlusion and predict the rigid transformation of each tooth during alignment. Our method achieves better results with the architecture built for tooth data and landmark constraints and has better explainability than previous methods with regard to clinical tooth alignments.

Structures and Electronic and Hydrogen Storage Properties of Magnesium Scandium Hydrides.

MgH2 is well known as a potential hydrogen storage material. However, its high thermodynamic stability, high dissociation temperature, slow absorption, and desorption kinetics severely limit its application. Aiming at these shortcomings, we try to improve the hydrogen storage property of MgH2 by doping with transition metal Sc atoms. The structures and electronic and hydrogen storage properties of Mg-Sc-H systems have been systematically studied by combining the crystal structure analysis by particle swarm optimization and density functional theory method. The results show that the structure of MgScH8 with the R3 space group is the most stable one, which is proved to be a wide-band gap (2.96 eV) semiconductor. The possible decomposition pathways, which are crucial for the applicability of R3-MgScH8 as a hydrogen storage material, are studied, and the pathway of MgScH8 → ScH6 + Mg + H2 is found to be the most favorable one under 107.8 GPa pressure, while above 107.8 GPa, MgScH8 → Mg + Sc + 4H2 becomes the most thermodynamically stable pathway and releases the maximum amount of hydrogen. Based on the root mean square deviation calculation, it is found that R3-MgScH8 begins to melt at 400 K. The result of ab initio molecular dynamics simulations shows that the hydrogen release capacity (4.04 wt %) can be easily achieved at 500 K, thus making MgScH8 a potential hydrogen storage material.

Robust Template Matching Using Multiple-Layered Absent Color Indexing.

Color is an essential feature in histogram-based matching. This can be extracted as statistical data during the comparison process. Although the applicability of color features in histogram-based techniques has been proven, position information is lacking during the matching process. We present a conceptually simple and effective method called multiple-layered absent color indexing (ABC-ML) for template matching. Apparent and absent color histograms are obtained from the original color histogram, where the absent colors belong to low-frequency or vacant bins. To determine the color range of compared images, we propose a total color space (TCS) that can determine the operating range of the histogram bins. Furthermore, we invert the absent colors to obtain the properties of these colors using threshold hT. Then, we compute the similarity using the intersection. A multiple-layered structure is proposed against the shift issue in histogram-based approaches. Each layer is constructed using the isotonic principle. Thus, absent color indexing and multiple-layered structure are combined to solve the precision problem. Our experiments on real-world images and open data demonstrated that they have produced state-of-the-art results. Moreover, they retained the histogram merits of robustness in cases of deformation and scaling.

Multifunctional SERS chip mediated by black phosphorus@gold-silver nanocomposites inserted in bilayer membrane for in-situ detection and degradation of hazardous materials.

Self-cleaning surface-enhanced Raman scattering (SERS) substrates dependent on versatile two-dimensional semiconductors offer an efficient channel for the sensitive monitoring and timely degradation of hazardous molecules. Herein, a kind of sophisticated SERS-active nanocomposites was developed by incorporating Au-Ag nanoparticles onto black phosphorus (BP) nanosheets via photo-induced self-reduction. Combining the substantial electromagnetic "hot spots" triggered by bimetallic plasma coupling effect and the efficient charge transfer from BP to probe molecules, the proposed nanocomposites featured attractive SERS enhancement, facilitating a limit of detection down to 4.5 × 10-10 M. Attributed to the remarkable restriction of electron-hole recombination stemming from "Schottky contact", the photocatalytic activity of BP was prominently boosted, demonstrating a complete degradation time as short as 65 min. Furthermore, the disgusting instability of BP was considerably hindered by inserting the nanocomposites into various bilayer matrices with diverse hardness and viscosity inspired by cling film principle. Moreover, a significantly elevated collection rate high to 93.1% for in-situ detection was also achieved by the as-manufactured flexible SERS chips based on tape. This study illustrates a clear perspective for the development of versatile BP-based SERS chips which might facilitate sensitive analysis and treatment of perilous contaminants in complicated real-life scenarios.