Integrating deep learning and data fusion for enhanced oranges soluble solids content prediction using machine vision and Vis/NIR spectroscopy.

The visible/near infrared (Vis/NIR) spectrum will become distorted due to variations in sample color, thereby reducing the prediction accuracy of fruit composition. In this study, we aimed to develop a deep learning model with color correction capability to predict oranges soluble solids content (SSC) based on multi-source data fusion. Initially, a machine vision and Vis/NIR spectroscopy online acquisition device was designed to collect and analyze color images and transmission spectra. Subsequently, data fusion methods were proposed for color features and spectral data. Finally, color-correction one-dimensional convolutional neural network (1D-CNN) models base on multi-source data were constructed. The results showed that, the RMSEP of optimal color-correction model was decreased by 36.4 % and 16.1 % compared to partial least squares model and conventional 1D-CNN model, respectively. The multi-source data fusion of machine vision and Vis/NIR spectroscopy has the potential to improve the accuracy of food composition prediction.

Behavior recognition of cage-free multi-broilers based on spatiotemporal feature learning.

Poultry behavior indicates their health, welfare, and production performance. Timely access to broilers' behavioral information can improve their welfare and reduce disease spread. Most behaviors require a period of observation before they can be accurately judged. However, the existing approaches for multi-object behavior recognition were mostly developed based on a single-frame image and ignored the temporal features in videos, which led to misrecognition. This study proposed an end-to-end method for recognizing multiple simultaneous behavioral events of cage-free broilers in videos by Broiler Behavior Recognition System (BBRS) based on spatiotemporal feature learning. The BBRS consisted of 3 main components: the improved YOLOv8s detector, the Bytetrack tracker, and the 3D-ResNet50-TSAM model. The basic network YOLOv8s was improved with MPDIoU to identify multiple broilers in the same frame of videos. The Bytetrack tracker was used to track each identified broiler and acquire its image sequence of 32 continuous frames as input for the 3D-ResNet50-TSAM model. To accurately recognize behavior of each tracked broiler, the 3D-ResNet50-TSAM model integrated a temporal-spatial attention module for learning the spatiotemporal features from its image sequence and enhancing inference ability in the case of its image sequence less than 32 continuous frames due to its tracker ID switching. Each component of BBRS was trained and tested with the rearing density of 7 to 8 birds/m2. The results demonstrated that the mAP@0.5 of the improved YOLOv8s detector was 99.50%. The Bytetrack tracker achieved a mean MOTA of 93.89% at different levels of occlusion. The Accuracy, Precision, Recall, and F1score of the 3D-ResNet50-TSAM model were 97.84, 97.72, 97.65, and 97.68%, respectively. The BBRS showed satisfactory inference ability with an Accuracy of 93.98% when 26 continuous frames of the tracked broiler were received by the 3D-ResNet50-TSAM model. This study provides an efficient tool for automatically and accurately recognizing behaviors of cage-free multi-broilers in videos. The code will be released on GitHub (https://github.com/CoderYLH/BBRS) as soon as the study is published.

Advancements and greenification potential of magnetic molecularly imprinted polymers for chromatographic analysis of veterinary drug residues in milk.

Milk, as a widely consumed nutrient-rich food, is crucial for bone health, growth, and overall nutrition. The persistent application of veterinary drugs for controlling diseases and heightening milk yield has imparted substantial repercussions on human health and environmental ecosystems. Due to the high demand, fresh consumption, complex composition of milk, and the potential adverse impacts of drug residues, advanced greener analytical methods are necessitated. Among them, functional materials-based analytical methods attract wide concerns. The magnetic molecularly imprinted polymers (MMIPs), as a kind of typical functional material, possess excellent greenification characteristics and potencies, and they are easily integrated into various detection technologies, which have offered green approaches toward analytes such as veterinary drugs in milk. Despite their increasing applications and great potential, MMIPs' use in dairy matrices remains underexplored, especially regarding ecological sustainability. This work reviews recent advances in MMIPs' synthesis and application as efficient sorbents for veterinary drug extraction in milk followed by chromatographic analysis. The uniqueness and effectiveness of MMIPs in real milk samples are evaluated, current limitations are addressed, and greenification opportunities are proposed. MMIPs show promise in revolutionizing green analytical procedures for veterinary drug detection, aligning with the environmental goals of modern food production systems.

Synthesis of green magnetic molecularly imprinted polymers for selective extraction of rifaximin in milk samples.

In this study, a new analytical method was developed using magnetic molecularly imprinted polymers (MMIPs) by employing eco-friendly supramolecular ternary deep eutectic solvents to synthesize these MMIPs for selective extraction of rifaximin. The characterization analysis and adsorption affinity investigation were conducted. The results showed fast adsorption (15 min) with high adsorption capacity (43.20 mg g-1) and selectivity for rifaximin. Various extraction parameters were optimized, achieving recoveries ranging from 86.67% to 99.47% in spiked milk samples using high-performance liquid chromatography (HPLC). The detection and quantification limits were 0.01 mg L-1 and 0.03 mg L-1, respectively. The method exhibited low RSDs (<4.70%) and excellent selectivity, with MMIPs reusable up to seven times with only a 10% performance loss. This study proposes a convenient and reliable method for trace-level rifaximin extraction from milk using eco-friendly MMIPs.

Rapid and In-Field Sensing of Hydrogen Peroxide in Plant by Hydrogel Microneedle Patch.

The rapidly changing climate is exacerbating the environmental stress that negatively impacts crop health and yield. Timely sensing of plant response to stress is beneficial to timely adjust planting conditions, promoting the healthy growth of plants, and improving plant productivity. Hydrogen peroxide (H2O2) is an important molecule of signal transduction in plants. However, the common methods for detecting H2O2  in plants are associated with certain drawbacks, such as long extraction time, cumbersome steps, dependence on large instruments, and difficulty in realizing in-field sensing. Therefore, it is urgent to establish more efficient detection methods to realize the rapid detection of H2O2 content in plants. In this research, poly (methyl vinyl ether-alt-maleic acid) (PMVE/MA) hydrogel microneedle (MN) patch for rapid extraction of leaf sap are prepared, and the extraction mechanism of PEG-crosslinked PMVE/MA hydrogel MN patch is studied. A method of rapid detection of H2O2 content in plants based on MN patch with optical detection technology is constructed. The hydrogel MN patch can be used for timely H2O2 analysis. This application enables new opportunities in plant engineering, and can be extended to the safety and health monitoring of other plants and animals.

Microwave-Enabled Fast Preparation of a Metal-Organic Framework Hybrid Membrane for Filtration-Enhanced Simultaneous Separation and Detection of Aflatoxin B1.

Mycotoxin contamination in food and the environment seriously harms human health. Sensitive and timely detection of mycotoxins is crucial. Here, we report a dual-functional hybrid membrane with absorptivity and responsiveness for fluorescent-quantitative detection of mycotoxin aflatoxin B1 (AFB1). A biomineralization-inspired and microwave-accelerated fabrication method was established to prepare a hybrid membrane with a metal-organic framework (MOF) loaded in high density. The MOF presented high efficiency in capturing AFB1 and showed fluorescence intensity alteration simultaneously, enabling a dual adsorption-response mode. Deriving from the inherent porous structure of the hybrid membrane and the absorptive/responsive ability of the loaded MOF, a filtration-enhanced detection mode was elaborated to provide a 1.67-fold signal increase compared with the conventional soaking method. Therefore, the hybrid membrane exhibited a rapid response time of 10 min and a low detection limit of 0.757 ng mL-1, superior to most analogues in rapidity and sensitivity. The hybrid membrane also presented superior specificity, reproducibility, and anti-interference ability and even performed well in extreme environments such as strong acid or alkaline, satisfying the practical requirements for facile and in-field detection. Therefore, the membrane had strong applicability in chicken feed samples, with a detection recovery between 70.6% and 101%. The hybrid membrane should have significant prospects in the rapid and in-field inspection of mycotoxins for agriculture and food.

A laser-Engraved Wearable Electrochemical Sensing Patch for Heat Stress Precise Individual Management of Horse.

In point-of-care diagnostics, the continuous monitoring of sweat constituents provides a window into individual's physiological state. For species like horses, with abundant sweat glands, sweat composition can serve as an early health indicator. Considering the salience of such metrics in the domain of high-value animal breeding, a sophisticated wearable sensor patch tailored is introduced for the dynamic assessment of equine sweat, offering insights into pH, potassium ion (K+), and temperature profiles during episodes of heat stress and under normal physiological conditions. The device integrates a laser-engraved graphene (LEG) sensing electrode array, a non-invasive iontophoretic module for stimulated sweat secretion, an adaptable signal processing unit, and an embedded wireless communication framework. Profiting from an admirable Truth Table capable of logical evaluation, the integrated system enabled the early and timely assessment for heat stress, with high accuracy, stability, and reproducibility. The sensor patch has been calibrated to align with the unique dermal and physiological contours of equine anatomy, thereby augmenting its applicability in practical settings. This real-time analysis tool for equine perspiration stands to revolutionize personalized health management approaches for high-value animals, marking a significant stride in the integration of smart technologies within the agricultural sector.

Skin-Inspired All-Natural Biogel for Bioadhesive Interface.

Natural material-based hydrogels are considered ideal candidates for constructing robust bio-interfaces due to their environmentally sustainable nature and biocompatibility. However, these hydrogels often encounter limitations such as weak mechanical strength, low water resistance, and poor ionic conductivity. Here, inspired by the role of natural moisturizing factor (NMF) in skin, a straightforward yet versatile strategy is proposed for fabricating all-natural ionic biogels that exhibit high resilience, ionic conductivity, resistance to dehydration, and complete degradability, without necessitating any chemical modification. A well-balanced combination of gelatin and sodium pyrrolidone carboxylic acid (an NMF compound) gives rise to a significant enhancement in the mechanical strength, ionic conductivity, and water retention capacity of the biogel compared to pure gelatin hydrogel. The biogel manifests temperature-controlled reversible fluid-gel transition properties attributed to the triple-helix junctions of gelatin, which enables in situ gelation on diverse substrates, thereby ensuring conformal contact and dynamic compliance with curved surfaces. Due to its salutary properties, the biogel can serve as an effective and biocompatible interface for high-quality and long-term electrophysiological signal recording. These findings provide a general and scalable approach for designing natural material-based hydrogels with tailored functionalities to meet diverse application needs.

Extrusion Printing of Surface-Functionalized Metal-Organic Framework Inks for a High-Performance Wearable Volatile Organic Compound Sensor.

Wearable sensors hold immense potential for real-time and non-destructive sensing of volatile organic compounds (VOCs), requiring both efficient sensing performance and robust mechanical properties. However, conventional colorimetric sensor arrays, acting as artificial olfactory systems for highly selective VOC profiling, often fail to meet these requirements simultaneously. Here, a high-performance wearable sensor array for VOC visual detection is proposed by extrusion printing of hybrid inks containing surface-functionalized sensing materials. Surface-modified hydrophobic polydimethylsiloxane (PDMS) improves the humidity resistance and VOC sensitivity of PDMS-coated dye/metal-organic frameworks (MOFs) composites. It also enhances their dispersion within liquid PDMS matrix, thereby promoting the hybrid liquid as high-quality extrusion-printing inks. The inks enable direct and precise printing on diverse substrates, forming a uniform and high particle-loading (70 wt%) film. The printed film on a flexible PDMS substrate demonstrates satisfactory flexibility and stretchability while retaining excellent sensing performance from dye/MOFs@PDMS particles. Further, the printed sensor array exhibits enhanced sensitivity to sub-ppm VOC levels, remarkable resistance to high relative humidity (RH) of 90%, and the differentiation ability for eight distinct VOCs. Finally, the wearable sensor proves practical by in situ monitoring of wheat scab-related VOC biomarkers. This study presents a versatile strategy for designing effective wearable gas sensors with widespread applications.

Magnetic Soft Microrobot Design for Cell Grasping and Transportation.

Manipulating cells at a small scale is widely acknowledged as a complex and challenging task, especially when it comes to cell grasping and transportation. Various precise methods have been developed to remotely control the movement of microrobots. However, the manipulation of micro-objects necessitates the use of end-effectors. This paper presents a study on the control of movement and grasping operations of a magnetic microrobot, utilizing only 3 pairs of electromagnetic coils. A specially designed microgripper is employed on the microrobot for efficient cell grasping and transportation. To ensure precise grasping, a bending deformation model of the microgripper is formulated and subsequently validated. To achieve precise and reliable transportation of cells to specific positions, an approach that combines an extended Kalman filter with a model predictive control method is adopted to accomplish the trajectory tracking task. Through experiments, we observe that by applying the proposed control strategy, the mean absolute error of path tracking is found to be less than 0.155 mm. Remarkably, this value accounts for only 1.55% of the microrobot's size, demonstrating the efficacy and accuracy of our control strategy. Furthermore, an experiment involving the grasping and transportation of a zebrafish embryonic cell (diameter: 800 µm) is successfully conducted. The results of this experiment not only validate the precision and effectiveness of the proposed microrobot and its associated models but also highlight its tremendous potential for cell manipulation in vitro and in vivo.

Integrated Fruit Ripeness Assessment System Based on an Artificial Olfactory Sensor and Deep Learning.

Artificial scent screening systems, inspired by the mammalian olfactory system, hold promise for fruit ripeness detection, but their commercialization is limited by low sensitivity or pattern recognition inaccuracy. This study presents a portable fruit ripeness prediction system based on colorimetric sensing combinatorics and deep convolutional neural networks (DCNN) to accurately identify fruit ripeness. Using the gas chromatography-mass spectrometry (GC-MS) method, the study discerned the distinctive gases emitted by mango, peach, and banana across various ripening stages. The colorimetric sensing combinatorics utilized 25 dyes sensitive to fruit volatile gases, generating a distinct scent fingerprint through cross-reactivity to diverse concentrations and varieties of gases. The unique scent fingerprints can be identified using DCNN. After capturing colorimetric sensor image data, the densely connected convolutional network (DenseNet) was employed, achieving an impressive accuracy rate of 97.39% on the validation set and 82.20% on the test set in assessing fruit ripeness. This fruit ripeness prediction system, coupled with a DCNN, successfully addresses the issues of complex pattern recognition and low identification accuracy. Overall, this innovative tool exhibits high accuracy, non-destructiveness, practical applicability, convenience, and low cost, making it worth considering and developing for fruit ripeness detection.

Recent advances in hydrogel microneedle-based biofluid extraction and detection in food and agriculture.

Microneedle (MN) technology has been extensively studied for its advantages of minimal invasiveness and user-friendliness. Notably, hydrogel microneedles (HMNs) have garnered considerable attention for biofluid extraction due to its high swelling properties and biocompatibility. This review provides a comprehensive overview of definition, materials, and fabrication methods associated with HMNs. The extraction mechanisms and optimization strategies for enhancing extraction efficiency are summarized. Moreover, particular emphasis is placed on HMN-based biofluid extraction and detection in the domains of food and agriculture, encompassing the detection of small molecules, nucleic acids, and other relevant analytes. Finally, current challenges and possible solutions associated with HMN-based biofluid extraction are discussed.

Dynamic and Label-Free Sensing of Cardiomyocyte Responses to Nanosized Vesicles for Cardiac Oxidative Stress Injury Therapy.

Cardiac oxidative stress is a significant phenotype of myocardial infarction disease, a leading cause of global health threat. There is an urgent need to develop innovative therapies. Nanosized extracellular vesicle (nEV)-based therapy shows promise, yet real-time monitoring of cardiomyocyte responses to nEVs remains a challenge. In this study, a dynamic and label-free cardiomyocyte biosensing system using microelectrode arrays (MEAs) was constructed. Cardiomyocytes were cultured on MEA devices for electrophysiological signal detection and treated with nEVs from E. coli, gardenia, HEK293 cells, and mesenchymal stem cells (MSC), respectively. E. coli-nEVs and gardenia-nEVs induced severe paroxysmal fibrillation, revealing distinct biochemical communication compared to MSC-nEVs. Principal component analysis identified variations and correlations between nEV types. MSC-nEVs enhanced recovery without inducing arrhythmias in a H2O2-induced oxidative stress injury model. This study establishes a fundamental platform for assessing biochemical communication between nEVs and cardiomyocytes, offering new avenues for understanding nEVs' functions in the cardiovascular system.

Implantable Electrochemical Microsensors for In Vivo Monitoring of Animal Physiological Information.

In vivo monitoring of animal physiological information plays a crucial role in promptly alerting humans to potential diseases in animals and aiding in the exploration of mechanisms underlying human diseases. Currently, implantable electrochemical microsensors have emerged as a prominent area of research. These microsensors not only fulfill the technical requirements for monitoring animal physiological information but also offer an ideal platform for integration. They have been extensively studied for their ability to monitor animal physiological information in a minimally invasive manner, characterized by their bloodless, painless features, and exceptional performance. The development of implantable electrochemical microsensors for in vivo monitoring of animal physiological information has witnessed significant scientific and technological advancements through dedicated efforts. This review commenced with a comprehensive discussion of the construction of microsensors, including the materials utilized and the methods employed for fabrication. Following this, we proceeded to explore the various implantation technologies employed for electrochemical microsensors. In addition, a comprehensive overview was provided of the various applications of implantable electrochemical microsensors, specifically in the monitoring of diseases and the investigation of disease mechanisms. Lastly, a concise conclusion was conducted on the recent advancements and significant obstacles pertaining to the practical implementation of implantable electrochemical microsensors.

Sub-second ultrafast yet programmable wet-chemical synthesis.

Wet-chemical synthesis via heating bulk solution is powerful to obtain nanomaterials. However, it still suffers from limited reaction rate, controllability, and massive consumption of energy/reactants, particularly for the synthesis on specific substrates. Herein, we present an innovative wet-interfacial Joule heating (WIJH) approach to synthesize various nanomaterials in a sub-second ultrafast, programmable, and energy/reactant-saving manner. In the WIJH, Joule heat generated by the graphene film (GF) is confined at the substrate-solution interface. Accompanied by instantaneous evaporation of the solvent, the temperature is steeply improved and the precursors are concentrated, thereby synergistically accelerating and controlling the nucleation and growth of nanomaterials on the substrate. WIJH leads to a record high crystallization rate of HKUST-1 (~1.97 µm s-1), an ultralow energy cost (9.55 × 10-6 kWh cm-2) and low precursor concentrations, which are up to 5 orders of magnitude faster, -6 and -2 orders of magnitude lower than traditional methods, respectively. Moreover, WIJH could handily customize the products' amount, size, and morphology via programming the electrified procedures. The as-prepared HKUST-1/GF enables the Joule-heating-controllable and low-energy-required capture and liberation towards CO2. This study opens up a new methodology towards the superefficient synthesis of nanomaterials and solvent-involved Joule heating.

Assessing the greenification potential of cyclodextrin-based molecularly imprinted polymers for pesticides detection.

Cyclodextrins, with their unparalleled attributes of eco-friendliness, natural abundance, versatile utility, and facile functionalization, make a paramount contribution to the field of molecular imprinting. Leveraging the unique properties of cyclodextrins in molecularly imprinted polymers synthesis has revolutionized the performance of molecularly imprinted polymers, resulting in enhanced adsorption selectivity, capacity, and rapid extraction of pesticides, while also circumventing conventional limitations. As the concern for food quality and safety continues to grow, the need for standard analytical methods to detect pesticides in food and environmental samples has become paramount. Cyclodextrins, being non-toxic and biodegradable, present an attractive option for greener reagents in imprinting polymers that can also ensure environmental safety post-application. This review provides a comprehensive summary of the significance of cyclodextrins in molecular imprinting for pesticide detection in food and environmental samples. The recent advancements in the synthesis and application of molecularly imprinted polymers using cyclodextrins have been critically analyzed. Furthermore, the current limitations have been meticulously examined, and potential opportunities for greenification with cyclodextrin applications in this field have been discussed. By harnessing the advantages of cyclodextrins in molecular imprinting, it is possible to develop highly selective and efficient methods for detecting pesticides in food and environmental samples while also addressing the challenges of sustainability and environmental impact.

Self-Illuminating NIR-II Chemiluminescence Nanosensor for In Vivo Tracking H2 O2 Fluctuation.

Chemiluminescence (CL) imaging, as an excitation-free technique, exhibits a markedly improved signal-to-noise ratio (SNR) owing to the absence of an excitation light source and autofluorescence interference. However, conventional chemiluminescence imaging generally focuses on the visible and first near-infrared (NIR-I) regions, which hinders high-performance biological imaging due to strong tissue scattering and absorption. To address the issue, self-luminescent NIR-II CL nanoprobes with a second near-infrared (NIR-II) luminescence in the presence of hydrogen peroxide are rationally designed. A cascade energy transfer, including chemiluminescence resonance energy transfer (CRET) from the chemiluminescent substrate to NIR-I organic molecules and Förster resonance energy transfer (FRET) from NIR-I organic molecules to NIR-II organic molecules, occurs in the nanoprobes, contributing to NIR-II light with great efficiency and good tissue penetration depth. Based on excellent selectivity, high sensitivity to hydrogen peroxide, and long-lasting luminescence performance, the NIR-II CL nanoprobes are applied to detect inflammation in mice, showing a 7.4-fold enhancement in SNR compared with that of fluorescence.

Metal-Organic Framework-Decorated Nanochannel Electrode: Integration of Internal Nanoconfined Space and Outer Surface for Small-Molecule Sensing.

Ionic current measurement has been the dominant signaling strategy in nanochannel-based sensors. However, the direct probing of the capture of small molecules is still challenging, and the sensing potential of the outer surface of nanochannels is always ignored. Here, we report the fabrication of an integrated nanochannel electrode (INCE) with nanoporous gold layers modified on two sides of nanochannels, and its application for small-molecule analysis was explored. Metal-organic frameworks (MOFs) were decorated inside and outside of nanochannels, enabling the reduction of pore size to several nanometers, which is among the thickness range of the electric double layer for confined ion diffusion. Combined with excellent adsorption characteristics of MOFs, the developed nanochannel sensor successfully constructed the internal nanoconfined space that could directly capture small molecules and instantly generate a current signal. The contribution of the outer surface and the internal nanoconfined space to diffusion suppression to electrochemical probes was investigated. We found that the constructed nanoelectrochemical cell was sensitive in both the inner channel and the outer surface, signifying a novel sensing mode with integration of the internal nanoconfined space and the outer surface of nanochannels. The MOF/INCE sensor showed excellent performance toward tetracycline (TC) with a detection limit of 0.1 ng·mL-1. Subsequently, sensitive and quantitative detection of TC down to 0.5 µg·kg-1 was achieved in actual chicken samples. This work may open up a new model of nanoelectrochemistry and provide an alternative solution in the field of nanopore analysis for small molecules.

Mobile robotics platform for strawberry temporal-spatial yield monitoring within precision indoor farming systems.

Plant phenotyping and production management are emerging fields to facilitate Genetics, Environment, & Management (GEM) research and provide production guidance. Precision indoor farming systems (PIFS), vertical farms with artificial light (aka plant factories) in particular, have long been suitable production scenes due to the advantages of efficient land utilization and year-round cultivation. In this study, a mobile robotics platform (MRP) within a commercial plant factory has been developed to dynamically understand plant growth and provide data support for growth model construction and production management by periodical monitoring of individual strawberry plants and fruit. Yield monitoring, where yield = the total number of ripe strawberry fruit detected, is a critical task to provide information on plant phenotyping. The MRP consists of an autonomous mobile robot (AMR) and a multilayer perception robot (MPR), i.e., MRP = the MPR installed on top of the AMR. The AMR is capable of traveling along the aisles between plant growing rows. The MPR consists of a data acquisition module that can be raised to the height of any plant growing tier of each row by a lifting module. Adding AprilTag observations (captured by a monocular camera) into the inertial navigation system to form an ATI navigation system has enhanced the MRP navigation within the repetitive and narrow physical structure of a plant factory to capture and correlate the growth and position information of each individual strawberry plant. The MRP performed robustly at various traveling speeds with a positioning accuracy of 13.0 mm. The temporal-spatial yield monitoring within a whole plant factory can be achieved to guide farmers to harvest strawberries on schedule through the MRP's periodical inspection. The yield monitoring performance was found to have an error rate of 6.26% when the plants were inspected at a constant MRP traveling speed of 0.2 m/s. The MRP's functions are expected to be transferable and expandable to other crop production monitoring and cultural tasks.

Enzymatic Catalysis in Size and Volume Dual-Confined Space of Integrated Nanochannel-Electrodes Chip for Enhanced Impedance Detection of Salmonella.

Nanochannel-based confinement effect is a fascinating signal transduction strategy for high-performance sensing, but only size confinement is focused on while other confinement effects are unexplored. Here, a highly integrated nanochannel-electrodes chip (INEC) is created and a size/volume-dual-confinement enzyme catalysis model for rapid and sensitive bacteria detection is developed. The INEC, by directly sandwiching a nanochannel chip (60 µm in thickness) in nanoporous gold layers, creates a micro-droplet-based confinement electrochemical cell (CEC). The size confinement of nanochannel promotes the urease catalysis efficiency to generate more ions, while the volume confinement of CEC significantly enriches ions by restricting diffusion. As a result, the INEC-based dual-confinement effects benefit a synergetic enhancement of the catalytic signal. A 11-times ion-strength-based impedance response is obtained within just 1 min when compared to the relevant open system. Combining this novel nanoconfinement effects with nanofiltration of INEC, a separation/signal amplification-integrated sensing strategy is further developed for Salmonella typhimurium detection. The biosensor realizes facile, rapid (<20 min), and specific signal readout with a detection limit of 9 CFU mL-1 in culturing solution, superior to most reports. This work may create a new paradigm for studying nanoconfined processes and contribute a new signal transduction technique for trace analysis application.