Safety Monitoring Applications Research Articles

Z-scheme heterostructure of ZnO@NPC/Au/ZnIn2S4 for chemical replacement reaction-mediated signal-on photoelectrochemical assay of mercury ion

Herein, an advanced Zn-based metal organic framework (Zn-MOF) derived ZnO embedded in a nitrogen-doped porous carbon (ZnO@NPC) polyhedron/Au/ three-dimensional (3D) chrysanthemum-like ZnIn2S Z-scheme heterojunction was employed for the chemical replacement reaction-mediated signal-on photoelectrochemical (PEC) sensing detection of mercury ions (Hg2+). The p-type ZnO@NPC polyhedron was initially prepared by carbonising the zeolitic imidazolate framework (ZIF)-8 polyhedron, followed by modification with Au nanoparticles (NPs) via an in situ photocatalytic reduction method. Subsequently, the ZnO@NPC/Au polyhedron was combined with n-type 3D chrysanthemum-like ZnIn2S4 through physical adsorption, forming the ZnO@NPC polyhedron/Au/3D chrysanthemum-like ZnIn2S4 Z-scheme heterostructure. The newly constructed heterostructure exhibited strong visible light–harvesting capacity, and considerably improved photoelectric conversion efficiency. The 3D chrysanthemum-like ZnIn2S4 functioned not only as a photosensitiser but also as an Hg2+ recognition probe. Importantly, the PEC sensor exhibited a considerably increased photocurrent response to Hg2+. This is presumably because the introduction of Hg2+ triggered a selective Zn-to-Hg chemical replacement reaction, leading to the in-situ formation of a ZnO@NPC/Au/ZnIn2S4/HgS heterojunction, which further improved charge separation. Consequently, Hg2+ was sensitively detected with a wide linear range from 0.5 pM to 2 μM and a low detection limit of 0.07 pM. Furthermore, the proposed sensor was validated by detecting Hg2+ in food and aqueous environments, indicating its potential application in food safety and environmental monitoring.

Room temperature detection of aspergillus flavus volatile organic compounds (VOCs) under simulated conditions using graphene oxide and tin oxide Nanorods (SnO2 NRs-GO)

This study investigated the application of a hybrid nanocomposite of tin oxide nanorods (SnO2 NRs) and graphene oxide (GO) for the chemoresistive detection of some volatile compounds (hexanal, benzaldehyde, octanal, 1-octanol, and ethyl acetate vapours) emitted by Aspergillus flavus under simulated conditions. The synthesised materials were characterised using various analytical techniques, including high-resolution transmission electron microscopy (HR-TEM), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, X-ray diffraction (XRD), Brunauer-Emmett-Teller (BET) analysis, and Fourier transform infrared spectroscopy (FTIR). Three sensors were fabricated: individual nanomaterials (i.e., SnO2 and GO) and composites (SnO2-GO). The results showed that SnO2 NRs had limited sensitivity as a sensor, while GO-based sensors responded to various analyte vapours. However, the incorporation of SnO2 NRs into GO layers resulted in synergistic effects and improved sensor performance. The sensors' sensitivity, selectivity, recovery, and response times were quantitatively determined from the sensors' response curves. The nanocomposite sensor demonstrated superior sensitivity and selectivity for analyte vapours with acceptable response and recovery times. In addition, the sensor was insensitive to humidity and showed robust performance up to 62% RH, although sensor drift occurred at 70% RH. This study highlights the promising potential of using SnO2 NRs-GO composite-based sensor for sensitive and selective detection of analyte vapours, which has significant implications for food safety and environmental monitoring applications.

Innovative Synthesis of Adhesive-Assisted and Recyclable Fe3O4@PD-Ag Photomagnetic Nanocomposites as SERS probes for Ultrasensitive Thiram Detection on Fruit Peels

Surface-enhanced Raman spectroscopy (SERS) with enormous advantages has emerged as a powerful tool for both highly sensitive structural detection of analytes and various imaging applications. A notable advancement in the realm of multifunctional SERS substrates is the integration of magnetic nanoparticles endowed with magnetic manipulation capabilities and noble metal nanoparticles exhibiting plasmonic resonance properties. This synergistic combination offers substantial practical benefits for trace analysis across diverse fields. In this paper, a new paradigm of Fe3O4@PEI-DTC-Ag (Fe3O4@PD-Ag) photomagnetic nanocomposites based on magnetic Fe3O4 nanoparticles (NPs), the noble metal silver quantum dots (Ag QDs) and the adhesive layer polyethyleneimine dithiocarbamate (PEI-DTC) for convenient recycling and ultrasensitive SERS detection was developed by engineering hydrothermal and seed-growth methods. The SERS performance of the Fe3O4@PD-Ag substrates with varying amounts of Ag was evaluated by detecting 4-aminothiophenol (4-ATP). Among the tested samples, Fe3O4@PD-Ag-4 with a silver solution volume of 200 ml exhibited the optimal performance, showcasing an enhancement factor (EF) of 4.418 × 105. Moreover, the relative standard deviations (RSDs) were calculated to be less than 11.5 %, demonstrating the SERS substrates exhibit good uniformity and spectral reproducibility. Of note, the SERS substrate has demonstrated successful trace detection of thiram molecules, achieving a limit of detection (LOD) of up to 10-9 M. The intensity of SERS signals shows a linear correlation with the logarithm of thiram concentration, ranging from 10-4 to 10-9 M, thus providing compelling evidence for quantitative detection. Importantly, the Fe3O4@PD-Ag-4 SERS substrate also showcases outstanding performance for detecting thiram in real food, underscoring its significant potential for application in food safety monitoring.

From antioxidant defense system damage to programmed cell apoptosis: Understanding lethal mechanisms of cypermethrin on fish kidneys

Harmful effects of pesticide pollution on aquaculture have become increasingly urgent. The kidney, critical organ of the endocrine system in fish, is susceptible to some pollutants in the aquatic system. This study aimed to investigate the comprehensive effects and toxicological mechanisms of cypermethrin (CMN) on fish kidneys. The findings derived from the in vivo experiments demonstrated that exposure to CMN resulted in notable damage to the kidneys of grass carp, accompanied by impairment to the antioxidant defense system of kidney tissue, inducing severe oxidative stress. The activities of T-SOD, CAT, and GSH decreased, and lipid peroxidation product MDA content increased sharply. Subsequently, p-PERK, p-eIF2α, GRP78, and IRE1 significantly increased, suggesting a disturbance in the endoplasmic reticulum (ER) homeostasis. In addition, excessive oxidative stress and ER stress initiated programmed apoptosis. At cellular level in vitro, the changes of CMN did on damage degree of grass carp kidney cells were verified. Toxicity mechanism was consistent with the in vivo test. The information provided herein emphasized the assessment of pollution caused by the toxic organic insecticide CMN effects on the food chain of marine organisms. The results of this study revealed potential nephrotoxic mechanisms of CMN in fish and provide essential data for the safety monitoring and rational application of CMN. Together, these results shed light on protein homeostasis mechanisms that may help manage misfolded proteins under oxidative stress conditions.

MOF-on-MOF heterostructure boosting AIE sensing and triggered structural collapse for histamine detection

We explored a novel preparation method for MOF-on-MOF heterostructured material (Zn-BTEC@ZIF-8). This prepared heterostructured material acts as a container, capable of adsorbing tetracycline hydrochloride molecules into its backbone through hydrogen bonding and π-π interactions. This phenomenon triggers an aggregation induced emission (AIE) effect, leading to the formation of luminescent bodies. The coordination between histamine and MOF was found to collapse the originally stabilized MOF-on-MOF structure. This collapse causes the splitting of the initially stabilized MOF-on-MOF structure from the aggregated state into fragments, resulting in the quenching of fluorescence in the fluorophore. Remarkably, the fluorescence quenching efficiency of this composite surpasses that of single-layer metal−organic framework (MOF) zeolitic imidazolate framework-8 (ZIF-8) or zinc-based MOF of pyromellitic acid (Zn-BTEC), enabling more sensitive detection of histamine. In this investigation, we constructed a label-free fluorescent sensor specifically designed for the detection of histamine, capitalizing on the AIE effect inherent in MOF-on-MOF architecture and the presence of tetracycline hydrochloride (Tet). The sensor demonstrates a rapid, straightforward, and stable response, allowing for histamine detection within 20 min. Notably, the sensor covers a detection range of 2–400 mg L−1, achieving a low detection limit of 1.458 mg L−1 The practical application of this sensor for quantitative detection of histamine in river water and various fish species exhibited robust performance, ensuring reliability and accuracy in real samples. Its potential application in food safety and environmental monitoring is evident, making it a valuable tool for addressing histamine-related challenges in these domains.

Safety assessment of sulfasalazine: a pharmacovigilance study based on FAERS database.

Sulfasalazine is a widely used anti-inflammatory medication for treating autoimmune disorders such as ulcerative colitis (UC), Crohn's disease, and rheumatoid arthritis. However, its safety profile has not been systematically evaluated in real-world settings. By analyzing the FDA Adverse Event Reporting System (FAERS) database, we identified risk signals associated with adverse reactions to sulfasalazine, offering valuable insights for clinical decision-making and risk management. Reports of adverse events (AEs) associated with sulfasalazine, covering the period from Q1 2004 to Q4 2023, were extracted from the FAERS database. Detailed case information was aggregated to assess demographic characteristics. The associations between sulfasalazine and adverse events were evaluated using the Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), Bayesian Confidence Propagation Neural Network (BCPNN), and Empirical Bayes Geometric Mean (EBGM). We extracted 7,156 adverse event reports from the FAERS database where sulfasalazine was identified as the "Primary Suspect (PS)" drug. Using disproportionality analysis, we identified 101 preferred terms (PT) related to sulfasalazine across 24 organ systems. Notable adverse reactions consistent with the drug's labeling were observed, including Stevens-Johnson syndrome, agranulocytosis, eosinophilic pneumonia, and crystalluria. Additionally, novel positive signals not previously documented in the drug label were identified, including acute febrile neutrophilic dermatosis, aseptic meningitis, glomerulonephritis, and hepatosplenic T-cell lymphoma. Most of the adverse reaction findings in this study are consistent with previous clinical research, and we have also identified new potential AEs associated with sulfasalazine. These findings provide valuable insights for the safety monitoring and clinical application of sulfasalazine.

Sol-gel immunoaffinity chromatography for isolation of deoxynivalenol and zearalenone from food and environmental samples

ABSTRACT The presence of mycotoxins in food is a significant concern due to their potential health risks. It is estimated that around 25% of global food production is contaminated with mycotoxins, which can be produced by certain types of fungi that infect crops during different stages of growth. As a result, it is crucial to monitor mycotoxins in food sources and develop effective methods for removal or reduction. One technique commonly used for detecting and separating mycotoxins is immunoaffinity chromatography (IAC). Traditionally, IAC uses antibodies covalently bonded to a solid support material, but this can lead to conformational changes and reduced affinity towards the analytes. To address these limitations, researchers have explored alternative methods for immobilising biomolecules, such as the sol-gel method, which involves encapsulating biomolecules within the pores of a glass matrix. This approach makes it easier to reuse the immunoaffinity columns and reduces the need for bacteriostatic agents, making the process more cost-effective and efficient. The results showed that sol-gel IAS was able to effectively separate these toxins from complex matrices, demonstrating its potential for widespread application in food safety monitoring. Additionally, the ability of sol-gel IAS to remove mycotoxins from environmental samples tested was highlighting its versatility and potential for broader applications beyond food safety. Finally, they demonstrated the compatibility of sol-gel IAS with high-performance liquid chromatography, enabling online coupling of the two techniques for improved efficiency and accuracy. The findings of this study suggest that sol-gel IAC is a promising method for the detection and separation of mycotoxins in various matrices, including food, environmental samples, and other biological fluids. Its advantages over traditional IAC methods make it an attractive option for large-scale analysis and monitoring of mycotoxins in diverse settings.

Gold nanoclusters anchored manganese dioxide nanocomposites with high structural stability for sensitive detection of methyl parathion

A long-standing goal has been to create a high-energy transfer system for food safety detection. Herein, gold nanoclusters-anchored manganese dioxide (AuNCs@MnO2) nanocomposites are designed by using co-template assembly. Bovine serum albumin is utilized as both skeletons of nucleation and stabilizer to guide the synthesis of the AuNCs@MnO2 nanocomposites under physiological conditions without the use of any potent oxidants and the hazardous organic solvent. The AuNCs@MnO2 nanocomposites exhibit good energy transfer efficiency and structural stability even under high salt conditions and a wide range of pH values. A sensitive sensing platform for the detection of methyl paraoxon has been constructed as a result of the excellent performance of AuNCs@MnO2 nanocomposites. Acetylcholinesterase catalyzes the conversion of the substrate to produce thiocholine, which induces the decomposition of MnO2 and the fluorescence recovery of AuNCs. Coupling with the pesticide inhibition effect, the platform can be employed to quantify methyl parathion with a detection limit of 3.1 pg mL−1. The AuNCs@MnO2-based platform displays high stability and anti-interference capability in food and environment samples, endowing practical application in food safety monitoring.

Target‐mediated suppression of rolling circle amplification for label‐free detection of aflatoxin B1

AbstractBackgroundAflatoxin B1 (AFB1) has been considered as a prevailing natural carcinogenic mycotoxin in various food and feedstuff products, posing serious endangers to human health. The quantitative and selective detection of AFB1 was significantly essential for food safety control.ResultsWe developed a label‐free fluorescent aptasensor using a target‐binding aptamer probe as the rolling circle amplification (RCA) primer for AFB1 detection. In the presence of AFB1, the probe preferentially combined with AFB1 rather than binding to the circular template, inhibiting the initiation of RCA process. The detection limit of the proposed method was as low as 6.6 nM, and the linear dynamic response range was four orders of magnitude wide. Besides, it revealed excellent specificity for AFB1 and high‐quality detection performance in food samples.ConclusionThe presented detection method was simple, rapid, and economical, which indicated a promising application in food safety monitoring.

An environment safety monitoring system for agricultural production based on artificial intelligence, cloud computing and big data networks

Monitoring the agricultural production environment is crucial for optimal crop growth and resource efficiency. Cloud Computing, Artificial Intelligence (AI), and Big Data have revolutionized traditional agriculture, promising improved output and product quality. The popularity of these technologies drives their application in safety monitoring. This system facilitates data collection and transmission among equipment, overcoming challenges of traditional systems like investment, costs, and maintenance. In this paper, cloud computing-based AI optimization technology and big data network were proposed to monitor the safety of the agricultural production environment, and the shortcomings of traditional distance vector hop (DV hop) positioning algorithms were analyzed in depth. RSSI (Received Signal Strength Indication) technology improved the traditional DV Hop location method. The paper analyses direct and indirect transmission for data transmission between WSN and cloud nodes and favors indirect transmission because it consumes less invalid energy. Finally, the article compares several evaluations of alternative algorithms for monitoring system performance, including data transmission reliability, data reception rate, and data delay. The experimental results in this paper showed that in the data reception rate test, the data reception rate of System 2 was 97% at the lowest and 99% at the highest, both exceeding 95%.

Computer Vision Applications In Construction And Asset Management Phases: A Literature Review

Recent advances in digital photography and unmanned aerial vehicle (UAV) platforms make visual data from construction project sites more accessible to project teams. To semi-automatically or automatically obtain the essential information, evaluate the ongoing activities or operations, and address project-level challenges, researchers have focused on applying various computer vision (CV)-based methods to process and interpret the acquired visual data. This research developed a framework to summarize the vision-based methods that have been applied to construction/asset management operations through a systematic literature review. The reviewed literature was composed of 103 journal papers from 2011 to 2020. All the reviewed journal papers were from the Ei Compendex database with specific search criteria. The developed framework consisted of two parts: use cases and CV domains. Use cases contained five aspects: safety monitoring, productivity improvement, progress monitoring, infrastructure inspection, and robotic application. CV domains contained six aspects: image processing, object classification, object detection, object tracking, pose estimation, and 3D reconstruction. All eleven aspects were integrated from the reviewed papers. For each reviewed paper, the general workflow of applied vision-based approaches was described and categorized into each use case. A trending timeline was developed to analyze the popularity of the identified use cases and CV domains within the reviewed time period. Both the quantity and variety of construction use cases and CV domains have increased. Challenges and limitations of applying CV-based methods in the construction industry were also identified. This paper provides readers with a summary of how CV-based methods have been used in the construction industry and serve as a reference for future research and development.

Excavator 3D pose estimation using deep learning and hybrid datasets

Earthwork operations are crucial parts of most construction projects. Heavy construction equipment and workers are often required to work in limited workspaces simultaneously. Struck-by accidents resulting from poor worker and equipment interactions account for a large proportion of accidents and fatalities on construction sites. The emerging technologies based on computer vision and artificial intelligence offer an opportunity to enhance construction safety through advanced monitoring utilizing site cameras. A crucial pre-requisite to the development of safety monitoring applications is the ability to identify accurately and localize the position of the equipment and its critical components in 3D space. This study proposes a workflow for excavator 3D pose estimation based on deep learning using RGB images. In the proposed workflow, an articulated 3D digital twin of an excavator is used to generate the necessary data for training a 3D pose estimation model. In addition, a method for generating hybrid datasets (simulation and laboratory) for adapting the 3D pose estimation model for various scenarios with different camera parameters is proposed. Evaluations prove the capability of the workflow in estimating the 3D pose of excavators. The study concludes by discussing the limitations and future research opportunities.

Dual-response fluorescent probe based on nitrogen-doped carbon dots and europium ions hybrid for ratiometric and on-site visual determination of oxytetracycline and tetracycline

Tetracyclines residues, particularly oxytetracycline (OTC) and tetracycline (TC), have raised extensive concern because of their serious adverse effects on human health. Herein, a dual-response fluorescent probe based on nitrogen-doped carbon dots (N-CDs) and Eu3+ hybrid (N-CDs–Eu3+) was developed to selectively determine OTC and TC. The N-CDs act as ancillary ligands of Eu3+ and recognition units of OTC/TC, while the Eu3+ ions chelated with N-CDs can also specifically recognize OTC/TC. Upon inclusion of OTC/TC, an enhancement in Eu3+ emission occurs due to the energy transfer from OTC/TC to Eu3+ and the efficient elimination of quenching effect caused by H2O molecule, which is attributed to the incorporation of N-CDs; while the blue fluorescence emitted by the N-CDs decreases under the inner filter effect and static quenching effect caused by OTC/TC. Based on the double and reverse response signals, the ratiometric detection of OTC and TC in the range of 0.1–45 μΜ and 0.1–30 μΜ is achieved with a detection limit of 0.017 and 0.041 μM, respectively. In addition, the noticeable variation in fluorescence color of the probe is integrated with a smartphone-assisted analysis device for the rapid on-site quantitative assay of OTC, where the detection limit is 0.15 μΜ. The results show that this probe performs with excellent specificity and anti-interference for both OTC and TC, and satisfactory detection results are obtained in lake water, milk, and honey samples, thereby confirming that the probe exhibits promising application in food safety and environmental monitoring.

MnO2 nanosheets anchored gold nanoclusters@ZIF-8 based ratiometric fluorescence sensor for monitoring chlorpyrifos degradation

Given the potential hazards of organophosphorus pesticides (OPs) to food safety, developing sensitive and reliable sensors for OPs is highly urgent. Herein, a new ratiometric fluorescence strategy with high sensitivity and good anti-interference ability is developed for monitoring chlorpyrifos based on manganese dioxide nanosheets anchored gold nanoclusters@zeolitic imidazolate framework-8 composite (MnO2-AuNCs@ZIF-8). The MnO2-AuNCs@ZIF-8 composite integrates the fluorescence property of AuNCs, the protection capacity of ZIF-8 and the dual function of MnO2 (quencher and inorganic catalyst), which enhanced sensing sensitivity via boosting the energy transfer and anti-interference ability by avoiding background effect. The fluorescence emission intensity of AuNCs@ZIF-8 at 635 nm and MnO2 catalytic products (2,3-diaminophenazine) at 535 nm can form a typical ratiometric with good anti-interference ability. MnO2-AuNCs@ZIF-8 composites can recognize the acetylcholinesterase hydrolysis products (thiocholine), owing to the analyte-initiated reduction of MnO2. Coupling with OPs inhibition effect, a ratiometric fluorescence platform is constructed for sensitive detection of chlorpyrifos with a detection limit of 0.22 ng mL−1. The ratiometric fluorescence sensor is effectively applied for precisely monitoring the residue and degradation of chlorpyrifos in pakchoi, endowing the practical application in food safety monitoring.

Dual-signal electrochemical aptasensor involving hybridization chain reaction amplification for aflatoxin B1 detection

Since aflatoxin B1 (AFB1) is a highly toxic and carcinogenic secondary metabolite, it is of great importance to develop an effective method for detection of it. Based on hybridization chain reaction (HCR), we proposed a ratiometric dual-signal electrochemical aptasensor (DECA) to realize rapid and sensitive detection of AFB1. In the aptasensor, ferrocene (Fc) and methylene blue (MB)-tagged single-stranded DNA sequences acted as dual-signal sources. Based on this, more stable hairpin DNA (HS-H1) and MB-labeled auxiliary hairpin DNA (H1 and H2) were introduced to extend the sensing platform. In presence of AFB1, it will compete with MB-cDNA and specifically combine with Fc-labeled aptamer. As a result, MB-cDNA will release and trigger the HCR for MB signal amplification with an enhancement in sensitivity. Besides, the ratio of current variations behaved a ratiometric signal output mode. In the proof-of-concept study, the single-signal mode behaved a linear range of 1 pM~10 nM with limit of detection (LOD) of 0.56 pM (IMB) and 0.15 pM (IFc), respectively, while the ratiometric detection mode showed the lowest LOD of 0.12 pM (S/N = 3). The good specificity, reproducibility, and stability of DECA further revealed its promising application in food safety monitoring.

Historical profile will tell? A deep learning-based multi-level embedding framework for adverse drug event detection and extraction

Analyzing adverse drug events (ADEs) is an integral part of drug safety monitoring, which plays a significant role in medication decision-making. The increasing prevalence of health-related social media may provide an avenue for drug safety profiling using patients' online posts. Recent advances in machine learning, especially in deep learning, have dramatically benefited ADE detection and extraction. However, despite the wide use of state-of-the-art deep learning models, prior research has predominantly analyzed each post independently instead of treating the relevant posts holistically. In this study, guided by the theories of transfer of learning, we adopted the design science research methodology and developed a deep learning-based approach by innovatively integrating historical profiles for ADE detection and extraction. Our framework—the Historical Awareness Multi-Level Embedding (HAMLE) model—outperformed existing state-of-the-art benchmarks by large margins. We also validated its real-world safety monitoring application using the Food and Drug Administration's drug safety warnings, and it showed promising performance in identifying previously unknown ADEs. This confirmed its potential for use as an early warning system for postmarketing surveillance. Furthermore, to evaluate the generalizability of HAMLE, we further tested it on a formal medical dataset extracted from PubMed, and it demonstrated robustness and achieved new state-of-the-art results. Our strategy of building a historical awareness deep learning model, inspired by extant theories, could create a new way to integrate historical profiles and characteristics to support various business tasks in different domains.

Combined Grey Wolf Optimizer Algorithm and Corrected Gaussian Diffusion Model in Source Term Estimation

It is extremely critical for an emergency response to quickly and accurately use source term estimation (STE) in the event of hazardous gas leakage. To determine the appropriate algorithm, four swarm intelligence optimization (SIO) algorithms including Gray Wolf optimizer (GWO), particle swarm optimization (PSO), genetic algorithm (GA) and ant colony optimization (ACO) are selected to be applied in STE. After calculation, all four algorithms can obtain leak source parameters. Among them, GWO and GA have similar computational efficiency, while ACO is computationally inefficient. Compared with GWO, GA and PSO, ACO requires larger population and more iterations to ensure accuracy of source parameters. Most notably, the convergence factor of GWO is self-adaptive, which is in favor of obtaining accurate results with lower population and iterations. On this basis, combination of GWO and a modified Gaussian diffusion model with surface correction factor is used to estimate the emission source term in this work. The calculation results demonstrate that the corrected Gaussian plume model can improve the accuracy of STE, which is promising for application in emergency warning and safety monitoring.

Node Collaborative Sensing-Based Redundant Path Construction for Multiarea Coverage in MWSNs

Mobile wireless sensor networks (MWSNs) have been widely used in environmental safety monitoring and other applications due to their many characteristics, especially in hazardous and harsh workplaces. This paper studies how to construct a covered redundant path between the Original Monitoring Area (OMA) and the Extended Area (EA) to provide continuous coverage and communication. To avoid the shortage of multi-area coverage control studies in MWSNs, we adopt a new Node Cooperative Sensing (NCS) model, and define the NCS-based Multi-area Coverage Redundant Path Construction (NCS-MCRPC) problem, intending to minimize the communication interruption of the path and ensure the monitoring of hazardous substances. To solve the NCS-MCRPC problem, we propose a Rapid Sensor Redeployment-based Redundant Path Construction (RSR-RPC) strategy. RSR-RPC includes the initialization phase, redeployment phase, working phase, and repair phase. To verify the effectiveness of the proposed mechanism, we conducted a series of experiments. The simulation results show that RSR-RPC outperforms the peer-to-peer algorithms in terms of path coverage performance and interruption probability.

Sequentially epitaxial growth multi-guest encapsulation strategy in MOF-on-MOF platform: Biogenic amine detection and systematic white light adjustment

MOF-on-MOF heterostructures possess multiple advantages inherited from various single-component metal–organic frameworks (MOFs). Here, we prepare a three-dimensional (3D) anionic fluorescence MOF {[In5(dpda)4(H2O)4]·H3O·3H2O}n (In-dpda) (H4dpda = 3-(3′,5′-dicarboxyphenyl)pyridine-2,6-dicarboxylic acid) with open 1D square channels. Due to the high lattice match (>97.8 %), classical ZIF-8 with powerful guest encapsulation ability controllably grows on the fluorescent core In-dpda to assemble a ZIF-8-on-In-dpda heterostructure through the sequentially epitaxial growth strategy. In addition, a smart ratiometric fluorescence sensor F-10c ⊂ ZIF-8-on-In-dpda is further constructed by synchronously encapsulating the fluorescein (F) during the epitaxial growth of ZIF-8 nanoparticles and presents high sensitivity to a biogenic amine with a low limit of detection of 4.7 nM at the 10 cycles assembly. The dual detection modes of both ratiometric fluorescent intensity and CIE chromaticity show a potential application in food safety monitoring. Meanwhile, multi-guest molecules F and rhodamine B are sequentially encapsulated in the guests ⊂ ZIF-8-on-In-dpda to achieve diverse luminescent colors including white light emission. This work presents a sequentially epitaxial growth strategy based on the lattice matching principle to assemble MOF-on-MOF platforms for multiple applications.

Biocompatible composite thin-film wearable piezoelectric pressure sensor for monitoring of physiological and muscle motions

Whereas piezoelectric pressure sensors (PPSs) have been applied in the monitoring of human body movement and physiological parameters, they show inherent limitations in wearable applications, including toxicity, degradation, and brittleness. In this study, we develop safe, stable, and mechanically flexible composite thin films consisting of polyvinylidene fluoride (PVDF), BaTiO3 nanoparticles (BTO-NPs), and textured aluminum nitride (AlN) thin film for the demonstration of wearable PPS with enhanced output performance and biocompatibility. The PPS made of BTO-NP-embedded-PVDF and AlN film on Cu foil is attached to different parts of human body to measure different output voltages depending on the physiological and physical stimulus. The simple bending (from breathing, chewing, and swallowing), joint motions (at wrist, elbow, and finger), and low- (from eyeball movement) and high-pressure applications (by squat, lunge, and walking) are measured. Our PVDF+BTO-NP/AlN-PPS (PBA-PPS) device has the potential for personal safety, healthcare, and activity monitoring applications with easy wearability.