Artificial sensing intelligence with silicon nanowires for ultraselective detection in the gas phase

Gas Sensing Properties of Indium-Oxide-Based Field-Effect Transistor: A Review

Excellent stability, low cost, high response, and sensitivity of indium oxide (In2O3), a metal oxide semiconductor, have been verified in the field of gas sensing. Conventional In2O3 gas sensors employ simple and easy-to-manufacture resistive components as transducers. However, the swift advancement of the Internet of Things has raised higher requirements for gas sensors based on metal oxides, primarily including lowering operating temperatures, improving selectivity, and realizing integrability. In response to these three main concerns, field-effect transistor (FET) gas sensors have garnered growing interest over the past decade. When compared with other metal oxide semiconductors, In2O3 exhibits greater carrier concentration and mobility. The property is advantageous for manufacturing FETs with exceptional electrical performance, provided that the off-state current is controlled at a sufficiently low level. This review presents the significant progress made in In2O3 FET gas sensors during the last ten years, covering typical device designs, gas sensing performance indicators, optimization techniques, and strategies for the future development based on In2O3 FET gas sensors.

Deep Learning-Based Kinetic Analysis in Paper-Based Analytical Cartridges Integrated with Field-Effect Transistors

This study explores the fusion of a field-effect transistor (FET), a paper-based analytical cartridge, and the computational power of deep learning (DL) for quantitative biosensing via kinetic analyses. The FET sensors address the low sensitivity challenge observed in paper analytical devices, enabling electrical measurements with kinetic data. The paper-based cartridge eliminates the need for surface chemistry required in FET sensors, ensuring economical operation (cost < $0.15/test). The DL analysis mitigates chronic challenges of FET biosensors such as sample matrix interference, by leveraging kinetic data from target-specific bioreactions. In our proof-of-concept demonstration, our DL-based analyses showcased a coefficient of variation of <6.46% and a decent concentration measurement correlation with an r2 value of >0.976 for cholesterol testing when blindly compared to results obtained from a CLIA-certified clinical laboratory. These integrated technologies have the potential to advance FET-based biosensors, potentially transforming point-of-care diagnostics and at-home testing through enhanced accessibility, ease-of-use, and accuracy.

A Strategy for Multigas Identification Using Multielectrical Parameters Extracted from a Single Carbon-Based Field-Effect Transistor Sensor

Given the widespread utilization of gas sensors across various industries, the detection of diverse and complex target gases presents a significant challenge in designing sensors with multigas detection capability. Although constructing a sensor array with widely used chemiresistive gas sensors is one solution, it is difficult for a single chemiresistive gas sensor to simultaneously detect different gases, as it can only detect a single target gas. The intrinsic reason for this bottleneck is that chemiresistive gas sensors rely entirely on the resistivity as the unique parameter to evaluate the diverse gas sensing properties of sensors, such as sensitivity, selectivity, etc. Herein, a field-effect transistor (FET) with abundant electrical parameters is employed to prepare a gas sensor for the detection of a variety of gases. Semiconducting carbon nanotubes (CNTs) are selected as the channel material, which is modified by Pd nanoparticles to enhance the gas sensing properties of the sensors. By extracting various electrical parameters such as transconductance, threshold voltage, etc. from the transfer characteristic curves of FET, a correlation between multielectrical parameters and various gas detection information is established for subsequent data analysis. Through the utilization of the principal component analysis algorithm, the identification of six gases can be finally achieved by relying solely on a single carbon-based FET-type gas sensor. We hope our work can solve the bottleneck of multigas identification by a single sensor in principle and is expected to reduce the system complexity and cost caused by the design of sensor arrays, offering a valuable guidance for multigas identification technology.

A pattern recognition artificial olfactory system based on human olfactory receptors and organic synaptic devices

Neuromorphic sensors, designed to emulate natural sensory systems, hold the promise of revolutionizing data extraction by facilitating rapid and energy-efficient analysis of extensive datasets. However, a challenge lies in accurately distinguishing specific analytes within mixtures of chemically similar compounds using existing neuromorphic chemical sensors. In this study, we present an artificial olfactory system (AOS), developed through the integration of human olfactory receptors (hORs) and artificial synapses. This AOS is engineered by interfacing an hOR-functionalized extended gate with an organic synaptic device. The AOS generates distinct patterns for odorants and mixtures thereof, at the molecular chain length level, attributed to specific hOR-odorant binding affinities. This approach enables precise pattern recognition via training and inference simulations. These findings establish a foundation for the development of high-performance sensor platforms and artificial sensory systems, which are ideal for applications in wearable and implantable devices.

Calibration of a Low-Cost Methane Sensor Using Machine Learning

In order to combat greenhouse gas emissions, the sources of these emissions must be understood. Environmental monitoring using low-cost wireless devices is one method of measuring emissions in crucial but remote settings, such as peatlands. The Figaro NGM2611-E13 is a low-cost methane detection module based around the TGS2611-E00 sensor. The manufacturer provides sensitivity characteristics for methane concentrations above 300 ppm, but lower concentrations are typical in outdoor settings. This study investigates the potential to calibrate these sensors for lower methane concentrations using machine learning. Models of varying complexity, accounting for temperature and humidity variations, were trained on over 50,000 calibration datapoints, spanning 0-200 ppm methane, 5-30 °C and 40-80% relative humidity. Interaction terms were shown to improve model performance. The final selected model achieved a root-mean-square error of 5.1 ppm and an R2 of 0.997, demonstrating the potential for the NGM2611-E13 sensor to measure methane concentrations below 200 ppm.

The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review

Disease diagnosis and monitoring using conventional healthcare services is typically expensive and has limited accuracy. Wearable health technology based on flexible electronics has gained tremendous attention in recent years for monitoring patient health owing to attractive features, such as lower medical costs, quick access to patient health data, ability to operate and transmit data in harsh environments, storage at room temperature, non-invasive implementation, mass scaling, etc. This technology provides an opportunity for disease pre-diagnosis and immediate therapy. Wearable sensors have opened a new area of personalized health monitoring by accurately measuring physical states and biochemical signals. Despite the progress to date in the development of wearable sensors, there are still several limitations in the accuracy of the data collected, precise disease diagnosis, and early treatment. This necessitates advances in applied materials and structures and using artificial intelligence (AI)-enabled wearable sensors to extract target signals for accurate clinical decision-making and efficient medical care. In this paper, we review two significant aspects of smart wearable sensors. First, we offer an overview of the most recent progress in improving wearable sensor performance for physical, chemical, and biosensors, focusing on materials, structural configurations, and transduction mechanisms. Next, we review the use of AI technology in combination with wearable technology for big data processing, self-learning, power-efficiency, real-time data acquisition and processing, and personalized health for an intelligent sensing platform. Finally, we present the challenges and future opportunities associated with smart wearable sensors.

The application of machine learning to air pollution research: A bibliometric analysis

Machine learning (ML) is an advanced computer algorithm that simulates the human learning process to solve problems. With an explosion of monitoring data and the increasing demand for fast and accurate prediction, ML models have been rapidly developed and applied in air pollution research. In order to explore the status of ML applications in air pollution research, a bibliometric analysis was made based on 2962 articles published from 1990 to 2021. The number of publications increased sharply after 2017, comprising approximately 75% of the total. Institutions in China and United States contributed half of all publications with most research being conducted by individual groups rather than global collaborations. Cluster analysis revealed four main research topics for the application of ML: chemical characterization of pollutants, short-term forecasting, detection improvement and optimizing emission control. The rapid development of ML algorithms has increased the capability to explore the chemical characteristics of multiple pollutants, analyze chemical reactions and their driving factors, and simulate scenarios. Combined with multi-field data, ML models are a powerful tool for analyzing atmospheric chemical processes and evaluating the management of air quality and deserve greater attention in future.

1D and 2D Field Effect Transistors in Gas Sensing: A Comprehensive Review

Rapid progress in the synthesis and fundamental understanding of 1D and 2D materials have solicited the incorporation of these nanomaterials into sensor architectures, especially field effect transistors (FETs), for the monitoring of gas and vapor in environmental, food quality, and healthcare applications. Yet, several challenges have remained unaddressed toward the fabrication of 1D and 2D FET gas sensors for real-field applications, which are related to properties, synthesis, and integration of 1D and 2D materials into the transistor architecture. This review paper encompasses the whole assortment of 1D-i.e., metal oxide semiconductors (MOXs), silicon nanowires (SiNWs), carbon nanotubes (CNTs)-and 2D-i.e., graphene, transition metal dichalcogenides (TMD), phosphorene-materials used in FET gas sensors, critically dissecting how the material synthesis, surface functionalization, and transistor fabrication impact on electrical versus sensing properties of these devices. Eventually, pros and cons of 1D and 2D FETs for gas and vapor sensing applications are discussed, pointing out weakness and highlighting future directions.

Low-Power, Multi-Transduction Nanosensor Array for Accurate Sensing of Flammable and Toxic Gases

Toxic and flammable gases pose a major safety risk in industrial settings; thus, their portable sensing is desired, which requires sensors with fast response, low-power consumption, and accurate detection. Herein, a low-power, multi-transduction array is presented for the accurate sensing of flammable and toxic gases. Specifically, four different sensors are integrated on a micro-electro-mechanical-systems platform consisting of bridge-type microheaters. To produce distinct fingerprints for enhanced selectivity, the four sensors operate based on two different transduction mechanisms: chemiresistive and calorimetric sensing. Local, in situ synthesis routes are used to integrate nanostructured materials (ZnO, CuO, and Pt Black) for the sensors on the microheaters. The transient responses of the four sensors are fed to a convolutional neural network for real-time classification and regression of five different gases (H₂ , NO₂ , C₂ H₆ O, CO, and NH₃ ). An overall classification accuracy of 97.95%, an average regression error of 14%, and a power consumption of 7 mW per device are obtained. The combination of a versatile low-power platform, local integration of nanomaterials, different transduction mechanisms, and a real-time machine learning strategy presented herein helps advance the constant need to simultaneously achieve fast, low-power, and selective gas sensing of flammable and toxic gases.

Large-scale nano-biosensing technologies

Nanoscale technologies have brought significant advancements to modern diagnostics, enabling unprecedented bio-chemical sensitivities that are key to disease monitoring. At the same time, miniaturized biosensors and their integration across large areas enabled tessellating these into high-density biosensing panels, a key capability for the development of high throughput monitoring: multiple patients as well as multiple analytes per patient. This review provides a critical overview of various nanoscale biosensing technologies and their ability to unlock high testing throughput without compromising detection resilience. We report on the challenges and opportunities each technology presents along this direction and present a detailed analysis on the prospects of both commercially available and emerging biosensing technologies.

Ratio Fluorescent Detecting of Tryptophan and Its Metabolite 5-Hydroxyindole-3-acetic Acid Relevant with Depression via Tb(III) Modified HOFs Hybrids: Further Designing Recyclable Molecular Logic Gate Connected by Back Propagation Neural Network

Exploring intelligent fluorescent materials with high reliability and precision to diagnose diseases is significant but remains a great challenge. Herein, based on coordination post-synthetic modification, a Tb³⁺ functionalized ME-PA (Tb@1) is prepared, which can emit brilliant green fluorescence through ligand-to-mental charge transfer-assisted energy transfer (LMCT-ET) process from ME-PA to Tb³⁺ ions. Tb@1 can simultaneously distinguish Tryptophan (Try) and its metabolite 5-hydroxyindole-3-acetic acid (5-HIAA), two effective indicators for depression, in ratio and colorimetric mode. And this sensor behaves the advantages of high efficiency and sensitivity, as well as excellent reusability and anti-interference. The PET process from ME to Try and 5-HIAA, and the competitive absorption between analytes and Tb@1 may be relevant to sensing mechanism. In realistic serum or urine environment, the detection limits of Tb@1 for Try and 5-HIAA are 0.0183 and 0.0149 mg L^-1 respectively. Moreover, in conjunction with back propagation neural network (BPNN), two dual-output molecular logic gates that can be calculated circularly are further designed, which realizes intelligent control of the electronic component to identify the existence of two biomarkers and judge their concentrations from fluorescence images. This work offers a novel approach to modulate logic circuits based on ML-assisted HOF fluorescent sensor, with promising application for a precise and pictorial depression diagnosis.

Artificially Intelligent Nanoarray Detects Various Cancers by Liquid Biopsy of Volatile Markers

Cancer is usually not symptomatic in its early stages. However, early detection can vastly improve prognosis. Liquid biopsy holds great promise for early detection, although it still suffers from many disadvantages, mainly searching for specific cancer biomarkers. Here, a new approach for liquid biopsies is proposed, based on volatile organic compound (VOC) patterns in the blood headspace. An artificial intelligence nanoarray based on a varied set of chemi-sensitive nano-based structured films is developed and used to detect and stage cancer. As a proof-of-concept, three cancer models are tested showing high incidence and mortality rates in the population: breast cancer, ovarian cancer, and pancreatic cancer. The nanoarray has >84% accuracy, >81% sensitivity, and >80% specificity for early detection and >97% accuracy, 100% sensitivity, and >88% specificity for metastasis detection. Complementary mass spectrometry analysis validates these results. The ability to analyze such a complex biological fluid as blood, while considering data of many VOCs at a time using the artificially intelligent nanoarray, increases the sensitivity of predictive models and leads to a potential efficient early diagnosis and disease-monitoring tool for cancer.

Room-Temperature ppb-Level H2S Gas Sensors Based on Ag Nanowire/Hollow PPy Nanotube Nanocomposites

H2S gas sensors were fabricated using Ag nanowire/hollow polypyrrole nanotube nanocomposite (Ag NW/hollow PPy NT) film for sensing ppb-level H2S gas at room temperature. The morphology, phase composition and crystalline structure of Ag NW/hollow PPy NT nanocomposites were analyzed via scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffractometry (XRD) and Fourier-transform infrared spectroscopy (FTIR). TEM and SEM images revealed that Ag NWs were well dispersed in the hollow PPy NT matrix. IR results showed no interaction between Ag NWs and hollow PPy NTs in the Ag NW/hollow PPy NT nanocomposites. The effect of the amount of added Ag NWs on the response of the Ag NW/hollow PPy NT nanocomposites to the ppb-level H2S gas was investigated. Comparative gas-sensing results revealed that the introduction of Ag NWs onto hollow PPy NTs was effective in promoting the sensor response to H2S gas. More importantly, the Ag NW/hollow PPy NT nanocomposite had a strong response to ppb-level H2S gas at room temperature.

Field-Effect Transistor-Based Biosensors for Environmental and Agricultural Monitoring

The precise monitoring of environmental contaminants and agricultural plant stress factors, respectively responsible for damages to our ecosystems and crop losses, has nowadays become a topic of uttermost importance. This is also highlighted by the recent introduction of the so-called "Sustainable Development Goals" of the United Nations, which aim at reducing pollutants while implementing more sustainable food production practices, leading to a reduced impact on all ecosystems. In this context, the standard methods currently used in these fields represent a sub-optimal solution, being expensive, laboratory-based techniques, and typically requiring trained personnel with high expertise. Recent advances in both biotechnology and material science have led to the emergence of new sensing (and biosensing) technologies, enabling low-cost, precise, and real-time detection. An especially interesting category of biosensors is represented by field-effect transistor-based biosensors (bio-FETs), which enable the possibility of performing in situ, continuous, selective, and sensitive measurements of a wide palette of different parameters of interest. Furthermore, bio-FETs offer the possibility of being fabricated using innovative and sustainable materials, employing various device configurations, each customized for a specific application. In the specific field of environmental and agricultural monitoring, the exploitation of these devices is particularly attractive as it paves the way to early detection and intervention strategies useful to limit, or even completely avoid negative outcomes (such as diseases to animals or ecosystems losses). This review focuses exactly on bio-FETs for environmental and agricultural monitoring, highlighting the recent and most relevant studies. First, bio-FET technology is introduced, followed by a detailed description of the the most commonly employed configurations, the available device fabrication techniques, as well as the specific materials and recognition elements. Then, examples of studies employing bio-FETs for environmental and agricultural monitoring are presented, highlighting in detail advantages and disadvantages of available examples. Finally, in the discussion, the major challenges to be overcome (e.g., short device lifetime, small sensitivity and selectivity in complex media) are critically presented. Despite the current limitations and challenges, this review clearly shows that bio-FETs are extremely promising for new and disruptive innovations in these areas and others.

Artificially Intelligent Olfaction for Fast and Noninvasive Diagnosis of Bladder Cancer from Urine

Globally, bladder cancer (BLC) is one of the most common cancers and has a high recurrence and mortality rate. Current clinical diagnostic approaches are either invasive or inaccurate. Here, we report on a cost-efficient, artificially intelligent chemiresistive sensor array made of polyaniline (PANI) derivatives that can noninvasively diagnose BLC at an early stage and maintain postoperative surveillance through ″smelling″ clinical urine samples at room temperature. In clinical trials, 18 healthy controls and 76 BLC patients (60 and 16 at early and advanced stages, respectively) are assessed by the artificial olfactory system. With the assistance of a support vector machine (SVM), very high sensitivity and accuracy from healthy controls are achieved, exceeding those obtained by the current techniques in practice. In addition, the recurrences of both early and advanced stages are diagnosed well, with the effect of confounding factors on the performance of the artificial olfactory system found to have a negligible influence on the diagnostic performance. Overall, this study contributes a novel, noninvasive, easy-to-use, inexpensive, real-time, accurate method for urine disease diagnosis, which can be useful for personalized care/diagnosis and postoperative surveillance, resulting in saving more lives.

Sensors for Volatile Organic Compounds

This paper provides an overview of recent developments in the field of volatile organic compound (VOC) sensors, which are finding uses in healthcare, safety, environmental monitoring, food and agriculture, oil industry, and other fields. It starts by briefly explaining the basics of VOC sensing and reviewing the currently available and quickly progressing VOC sensing approaches. It then discusses the main trends in materials' design with special attention to nanostructuring and nanohybridization. Emerging sensing materials and strategies are highlighted and their involvement in the different types of sensing technologies is discussed, including optical, electrical, and gravimetric sensors. The review also provides detailed discussions about the main limitations of the field and offers potential solutions. The status of the field and suggestions of promising directions for future development are summarized.

Engineered Nanotechnology: An Effective Therapeutic Platform for the Chronic Cutaneous Wound

The healing of chronic wound infections, especially cutaneous wounds, involves a complex cascade of events demanding mutual interaction between immunity and other natural host processes. Wound infections are caused by the consortia of microbial species that keep on proliferating and produce various types of virulence factors that cause the development of chronic infections. The mono- or polymicrobial nature of surface wound infections is best characterized by its ability to form biofilm that renders antimicrobial resistance to commonly administered drugs due to poor biofilm matrix permeability. With an increasing incidence of chronic wound biofilm infections, there is an urgent need for non-conventional antimicrobial approaches, such as developing nanomaterials that have intrinsic antimicrobial-antibiofilm properties modulating the biochemical or biophysical parameters in the wound microenvironment in order to cause disruption and removal of biofilms, such as designing nanomaterials as efficient drug-delivery vehicles carrying antibiotics, bioactive compounds, growth factor antioxidants or stem cells reaching the infection sites and having a distinct mechanism of action in comparison to antibiotics-functionalized nanoparticles (NPs) for better incursion through the biofilm matrix. NPs are thought to act by modulating the microbial colonization and biofilm formation in wounds due to their differential particle size, shape, surface charge and composition through alterations in bacterial cell membrane composition, as well as their conductivity, loss of respiratory activity, generation of reactive oxygen species (ROS), nitrosation of cysteines of proteins, lipid peroxidation, DNA unwinding and modulation of metabolic pathways. For the treatment of chronic wounds, extensive research is ongoing to explore a variety of nanoplatforms, including metallic and nonmetallic NPs, nanofibers and self-accumulating nanocarriers. As the use of the magnetic nanoparticle (MNP)-entrenched pre-designed hydrogel sheet (MPS) is found to enhance wound healing, the bio-nanocomposites consisting of bacterial cellulose and magnetic nanoparticles (magnetite) are now successfully used for the healing of chronic wounds. With the objective of precise targeting, some kinds of "intelligent" nanoparticles are constructed to react according to the required environment, which are later incorporated in the dressings, so that the wound can be treated with nano-impregnated dressing material in situ. For the effective healing of skin wounds, high-expressing, transiently modified stem cells, controlled by nano 3D architectures, have been developed to encourage angiogenesis and tissue regeneration. In order to overcome the challenge of time and dose constraints during drug administration, the approach of combinatorial nano therapy is adopted, whereby AI will help to exploit the full potential of nanomedicine to treat chronic wounds.

Breath Analysis: A Promising Tool for Disease Diagnosis-The Role of Sensors

Early-stage disease diagnosis is of particular importance for effective patient identification as well as their treatment. Lack of patient compliance for the existing diagnostic methods, however, limits prompt diagnosis, rendering the development of non-invasive diagnostic tools mandatory. One of the most promising non-invasive diagnostic methods that has also attracted great research interest during the last years is breath analysis; the method detects gas-analytes such as exhaled volatile organic compounds (VOCs) and inorganic gases that are considered to be important biomarkers for various disease-types. The diagnostic ability of gas-pattern detection using analytical techniques and especially sensors has been widely discussed in the literature; however, the incorporation of novel nanomaterials in sensor-development has also proved to enhance sensor performance, for both selective and cross-reactive applications. The aim of the first part of this review is to provide an up-to-date overview of the main categories of sensors studied for disease diagnosis applications via the detection of exhaled gas-analytes and to highlight the role of nanomaterials. The second and most novel part of this review concentrates on the remarkable applicability of breath analysis in differential diagnosis, phenotyping, and the staging of several disease-types, which are currently amongst the most pressing challenges in the field.

Mechanistic Approach for Long-Term Stability of a Polyethylene Glycol-Carbon Black Nanocomposite Sensor

Polymer-carbon nanocomposite sensor is a promising molecular sensing device for electronic nose (e-nose) due to its printability, variety of polymer materials, and low operation temperature; however, the lack of stability in an air environment has been an inevitable issue. Here, we demonstrate a design concept for realizing long-term stability in a polyethylene glycol (PEG)-carbon black (CB) nanocomposite sensor by understanding the underlying phenomena that cause sensor degradation. Comparison of the sensing properties and infrared spectroscopy on the same device revealed that the oxidation-induced consumption of PEG is a crucial factor for the sensor degradation. According to the mechanism, we introduced an antioxidizing agent (i.e., ascorbic acid) into the PEG-CB nanocomposite sensor to suppress the PEG oxidation and successfully demonstrated the long-term stability of sensing properties under an air environment for 30 days, which had been difficult in conventional polymer-carbon nanocomposite sensors.

Smart Materials Enabled with Artificial Intelligence for Healthcare Wearables

Contemporary medicine suffers from many shortcomings in terms of successful disease diagnosis and treatment, both of which rely on detection capacity and timing. The lack of effective, reliable, and affordable detection and real‐time monitoring limits the affordability of timely diagnosis and treatment. A new frontier that overcomes these challenges relies on smart health monitoring systems that combine wearable sensors and an analytical modulus. This review presents the latest advances in smart materials for the development of multifunctional wearable sensors while providing a bird's eye‐view of their characteristics, functions, and applications. The review also presents the state‐of‐the‐art on wearables fitted with artificial intelligence (AI) and support systems for clinical decision in early detection and accurate diagnosis of disorders. The ongoing challenges and future prospects for providing personal healthcare with AI‐assisted support systems relating to clinical decisions are presented and discussed.

Nanowire-based sensor electronics for chemical and biological applications

Detection and recognition of chemical and biological species via sensor electronics are important not only for various sensing applications but also for fundamental scientific understanding. In the past two decades, sensor devices using one-dimensional (1D) nanowires have emerged as promising and powerful platforms for electrical detection of chemical species and biologically relevant molecules due to their superior sensing performance, long-term stability, and ultra-low power consumption. This paper presents a comprehensive overview of the recent progress and achievements in 1D nanowire synthesis, working principles of nanowire-based sensors, and the applications of nanowire-based sensor electronics in chemical and biological analytes detection and recognition. In addition, some critical issues that hinder the practical applications of 1D nanowire-based sensor electronics, including device reproducibility and selectivity, stability, and power consumption, will be highlighted. Finally, challenges, perspectives, and opportunities for developing advanced and innovative nanowire-based sensor electronics in chemical and biological applications are featured.

Multifunctional Dressing for Wound Diagnosis and Rehabilitation

A wound dressing is a sterile pad or compress that is used in direct contact with a wound to help it heal and prevent further issues or complications. Though wound healing is an intricate dynamic process that involves multiple biomolecular species, conventional wound dressings have a limited ability to provide timely information of abnormal conditions, missing the best time for early treatment. The current perspective presents and discusses the design and development of smart wound dressings that are integrated with multifunctional materials, wearable sensors and drug delivery systems as well as their application ranging from wound monitoring to timely application of therapeutics. The perspective also discusses the ongoing challenges and exciting opportunities associated with the development of wearable sensor-based smart wound dressing and provide critical insights into wound healing monitoring and management.

A flexible virtual sensor array based on laser-induced graphene and MXene for detecting volatile organic compounds in human breath

Detecting volatile organic compounds (VOCs) in human breath is critical for the early diagnosis of diseases. Good selectivity of VOC sensors is crucial for the accurate analysis of VOC biomarkers in human breath, which consists of more than 200 types of VOCs. In this paper, a flexible virtual sensor array (FVSA) was proposed based on a sensing layer of MXene and laser-induced graphene interdigital electrodes (LIG-IDEs) for detecting VOCs in exhaled human breath. The fabrication of LIG-IDEs avoids the costly and complicated procedures required for the preparation of traditional IDEs. The FVSA's responses of multiple parameters help build a unique fingerprint for each VOC, without a need for changing the temperature of the sensing element, which is commonly used in the VSA of semiconductor VOC sensors. Based on machine learning algorithms, we have achieved highly precise recognition of different VOCs and mixtures and accurate prediction (accuracy of 89.1%) of the objective VOC's concentration in variable backgrounds using this proposed FVSA. Moreover, a blind analysis validates the capacity of the FVSA to identify alcohol content in human breath with an accuracy of 88.9% using breath samples from volunteers before and after alcohol consumption. These results show that the proposed FVSA is promising for the detection of VOC biomarkers in human exhaled breath and early diagnosis of diseases.

Recent Advances in Silicon FET Devices for Gas and Volatile Organic Compound Sensing

Highly sensitive and selective gas and volatile organic compound (VOC) sensor platforms with fast response and recovery kinetics are in high demand for environmental health monitoring, industry, and medical diagnostics. Among the various categories of gas sensors studied to date, field effect transistors (FETs) have proved to be an extremely efficient platform due to their miniaturized form factor, high sensitivity, and ultra-low power consumption. Despite the advent of various kinds of new materials, silicon (Si) still enjoys the advantages of excellent and reproducible electronic properties and compatibility with complementary metal–oxide–semiconductor (CMOS) technologies for integrated multiplexing and signal processing. This review gives an overview of the recent developments in Si FETs for gas and VOC sensing. We categorised the Si FETs into Si nanowire (NW) FETs; planar Si FETs, in which the Si channel is either a part of the silicon on insulator (SOI) or the bulk Si, as in conventional FETs; and electrostatically formed nanowire (EFN) FETs. The review begins with a brief introduction, followed by a description of the Si NW FET gas and VOC sensors. A brief description of the various fabrication strategies of Si NWs and the several functionalisation methods to improve the sensing performances of Si NWs are also provided. Although Si NW FETs have excellent sensing properties, they are far from practical realisation due to the extensive fabrication procedures involved, along with other issues that are critically assessed briefly. Then, we describe planar Si FET sensors, which are much closer to real-world implementation. Their simpler device architecture combined with excellent sensing properties enable them as an efficient platform for gas sensing. The third category, the EFN FET sensors, proved to be another potential platform for gas sensing due to their intriguing properties, which are elaborated in detail. Finally, the challenges and future opportunities for gas sensing are addressed.

Chemical Gas Sensors: Recent Developments, Challenges, and the Potential of Machine Learning-A Review

Nowadays, there is increasing interest in fast, accurate, and highly sensitive smart gas sensors with excellent selectivity boosted by the high demand for environmental safety and healthcare applications. Significant research has been conducted to develop sensors based on novel highly sensitive and selective materials. Computational and experimental studies have been explored in order to identify the key factors in providing the maximum active location for gas molecule adsorption including bandgap tuning through nanostructures, metal/metal oxide catalytic reactions, and nano junction formations. However, there are still great challenges, specifically in terms of selectivity, which raises the need for combining interdisciplinary fields to build smarter and high-performance gas/chemical sensing devices. This review discusses current major gas sensing performance-enhancing methods, their advantages, and limitations, especially in terms of selectivity and long-term stability. The discussion then establishes a case for the use of smart machine learning techniques, which offer effective data processing approaches, for the development of highly selective smart gas sensors. We highlight the effectiveness of static, dynamic, and frequency domain feature extraction techniques. Additionally, cross-validation methods are also covered; in particular, the manipulation of the k-fold cross-validation is discussed to accurately train a model according to the available datasets. We summarize different chemresistive and FET gas sensors and highlight their shortcomings, and then propose the potential of machine learning as a possible and feasible option. The review concludes that machine learning can be very promising in terms of building the future generation of smart, sensitive, and selective sensors.

Ab Initio Design, Shaping, and Assembly of Free-Standing Silicon Nanoprobes

Free-standing silicon nanoprobes (SiNPs) are critical tools for intracellular bioelectrical signal recording, while a scalable fabrication of these tiny SiNPs with ab initio geometry designs has not been possible. In this work, we demonstrate a novel growth shaping of slim Si nanowires (SiNWs) into SiNPs with sharp tips (curvature radii <300 nm), tunable angles of 30°, 60°, to 120° and even programmable triangle/circular shapes. A precise growth integration of orderly single, double, and quadruple SiNPs at prescribed locations enables convenient electrode connection, transferring and mounting these tiny tips onto movable arms to serve as long-protruding (over 4-20 μm) nanoprobes. Mechanical flexibility, resilience, and field-effect sensing functionality of the SiNPs were systematically testified in liquid nanodroplet and cell environments. This highly reliable and economic manufacturing of advanced SiNPs holds a strong potential to boost and open up the market implementations of a wide range of intracellular sensing, monitoring, and editing applications.

Functionalization and Characterization of Silicon Nanowires for Sensing Applications: A Review

Silicon nanowires are attractive materials from the point of view of their electrical properties or high surface-to-volume ratio, which makes them interesting for sensing applications. However, they can achieve a better performance by adjusting their surface properties with organic/inorganic compounds. This review gives an overview of the main techniques used to modify silicon nanowire surfaces as well as characterization techniques. A comparison was performed with the functionalization method developed, and some applications of modified silicon nanowires and their advantages on those non-modified are subsequently presented. In the final words, the future opportunities of functionalized silicon nanowires for chipless tag radio frequency identification (RFID) have been depicted.

Artificial Intelligence in Medical Sensors for Clinical Decisions

Due to the limited ability of conventional methods and the limited perspective of human diagnostics, patients are often diagnosed incorrectly or at a late stage as their disease condition progresses. They may then undergo unnecessary treatments due to inaccurate diagnoses. In this Perspective, we offer a brief overview on the integration of nanotechnology-based medical sensors and artificial intelligence (AI) for advanced clinical decision support systems to help decision-makers and healthcare systems improve how they approach information, insights, and the surrounding contexts, as well as to promote the uptake of personalized medicine on an individualized basis. Relying on these milestones, wearable sensing devices could enable interactive and evolving clinical decisions that could be used for evidence-based analysis and recommendations as well as for personalized monitoring of disease progress and treatment. We present and discuss the ongoing challenges and future opportunities associated with AI-enabled medical sensors in clinical decisions.

Point-of-care test system for detection of immunoglobulin-G and -M against nucleocapsid protein and spike glycoprotein of SARS-CoV-2

The coronavirus disease 2019 (COVID-19) epidemic continues to ravage the world. In epidemic control, dealing with a large number of samples is a huge challenge. In this study, a point-of-care test (POCT) system was successfully developed and applied for rapid and accurate detection of immunoglobulin-G and -M against nucleocapsid protein (anti-N IgG/IgM) and receptor-binding domain in spike glycoprotein (anti-S-RBD IgG/IgM) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Any one of the IgG/IgM found in a sample was identified as positive. The POCT system contains colloidal gold-based lateral flow immunoassay test strips, homemade portable reader, and certified reference materials, which detected anti-N and anti-S-RBD IgG/IgM objectively in serum within 15 min. Receiver operating characteristic curve analysis was used to determine the optimal cutoff values, sensitivity, and specificity. It exhibited equal to or better performances than four approved commercial kits. Results of the system and chemiluminescence immunoassay kit detecting 108 suspicious samples had high consistency with kappa coefficient at 0.804 (P < 0.001). Besides, the levels and alterations of the IgG/IgM in an inpatient were primarily investigated by the POCT system. Those results suggested the POCT system possess the potential to contribute to rapid and accurate serological diagnosis and epidemiological survey of COVID-19.

Self-template-derived ZnCo2O4 porous microspheres decorated by Ag nanoparticles and their selective detection of formaldehyde

ZnCo₂O₄ porous microspheres were successfully synthesized through a facile one-step solvent method using polyethylene glycol 1000 as a self-assembly template and subsequent annealing.

Enabling Ultrahigh-Aspect-Ratio Silicon Nanowires Using Precise Experiments for Detecting the Onset of Collapse

Top-down patterning along with metal-assisted chemical etching (MACE) can enable the fabrication of highly controlled wafer-scale silicon nanowires (Si-NWs). Maximizing the NW aspect ratio, while avoiding collapse, can enable many important applications. A precise experimental technique has been developed here to study the onset of Si-NW collapse. This experimental approach has resulted in unexpectedly tall Si-NWs for oversized wires separated by sub-50-nm gaps. As compared to known theory, a factor of 4.5 increase in maximum aspect ratio was achieved for uncollapsed nanowires with 200-nm pitch and 25-nm spacing. This discrepancy between known theory and experimental results was eliminated when the gold-resist caps (which are a feature of our MACE process) on top of these nanowires were removed. This led us to incorporate electrostatic repulsion into known theoretical formulations, which matched the experimental results. In summary, this work provides new experimental and theoretical insights into nanowire collapse behavior.

Silicon Nanowires for Gas Sensing: A Review

The unique electronic properties of semiconductor nanowires, in particular silicon nanowires (SiNWs), are attractive for the label-free, real-time, and sensitive detection of various gases. Therefore, over the past two decades, extensive efforts have been made to study the gas sensing function of NWs. This review article presents the recent developments related to the applications of SiNWs for gas sensing. The content begins with the two basic synthesis approaches (top-down and bottom-up) whereby the advantages and disadvantages of each approach have been discussed. Afterwards, the basic sensing mechanism of SiNWs for both resistor and field effect transistor designs have been briefly described whereby the sensitivity and selectivity to gases after different functionalization methods have been further presented. In the final words, the challenges and future opportunities of SiNWs for gas sensing have been discussed.

Bio-inspired gas sensing: boosting performance with sensor optimization guided by "machine learning"

The performance of existing gas sensors often degrades in field conditions because of the loss of measurement accuracy in the presence of interferences. Thus, new sensing approaches are required with improved sensor selectivity. We are developing a new generation of gas sensors, known as multivariable sensors, that have several independent responses for multi-gas detection with a single sensor. In this study, we analyze the capabilities of natural and fabricated photonic three-dimensional (3-D) nanostructures as sensors for the detection of different gaseous species, such as vapors and non-condensable gases. We employed bare Morpho butterfly wing scales to control their gas selectivity with different illumination angles. Next, we chemically functionalized Morpho butterfly wing scales with a fluorinated silane to boost the response of these nanostructures to the vapors of interest and to suppress the response to ambient humidity. Further, we followed our previously developed design rules for sensing nanostructures and fabricated bioinspired inorganic 3-D nanostructures to achieve functionality beyond natural Morpho scales. These fabricated nanostructures have embedded catalytically active gold nanoparticles to operate at high temperatures of ≈300 °C for the detection of gases for solid oxide fuel cell (SOFC) applications. Our performance advances in the detection of multiple gaseous species with specific nanostructure designs were achieved by coupling the spectral responses of these nanostructures with machine learning (a.k.a. multivariate analysis, chemometrics) tools. Our newly acquired knowledge from studies of these natural and fabricated inorganic nanostructures coupled with machine learning data analytics allowed us to advance our design rules for sensing nanostructures toward the required gas selectivity for numerous gas monitoring scenarios at room and high temperatures for industrial, environmental, and other applications.

Buffered Oxide Etchant Post-Treatment of a Silicon Nanofilm for Low-Cost and Performance-Enhanced Chemical Sensors

The high surface-to-volume ratio of nanostructured materials is the key factor for excellent performance when applied to chemical sensors. In order to achieve this by a facile and low-cost fabrication strategy, buffered oxide etchant (BOE) treatment of a silicon (Si)-based sensor was proposed. An n⁺-n^--n⁺ Si nanofilm structure was treated with a BOE, and palladium nanoparticles (PdNPs) were coated on the n-type Si channel surface via short-time electron beam evaporation to enable a highly sensitive and selective sensing of hydrogen (H₂) gas. The BOE treatment effect on lightly doped n-type Si was investigated, and the surface morphology of the etched Si was analyzed. Furthermore, the H₂ sensing characterization of PdNP-decorated Si devices with various BOE treatment times was systematically evaluated at room temperature. The results revealed that the surface of n-type Si is roughened by BOE treatment, which can further enhance the H₂-sensing performance of Pd-decorated Si. The elaborate study on the BOE-post-treated Si H₂ sensor showed that the performance enhancement was stable. The BOE treatment strategy was also applied to the nanopatterned Si sensors, which induced a clear performance enhancement for the H₂ sensing.

Review of Gravimetric Sensing of Volatile Organic Compounds

Volatile organic compounds (VOCs) are pervasive in the environment. Since the early 1980s, substantial work has examined the detection of these materials, as they can indicate environmental changes that can affect human health. VOCs and similar compounds present a very specific sensing problem in that they are not reactive and often nonpolar, so it is difficult to find materials that selectively bind or adsorb them. A number of techniques are applied to vapor sensing. High resolution molecular separation approaches such as gas chromatography and mass spectrometry are well-characterized and offer high sensitivity, but are difficult to implement in portable, real-time monitors, whereas approaches such as chemiresistors are promising, but still in development. Gravimetric approaches, in which the mass of an adsorbed vapor is directly measured, have several potential advantages over other techniques but have so far lagged behind other approaches in performance and market penetration. This review aims to offer a comprehensive background on gravimetric sensing including underlying resonators and sensitizers, as well as a picture of applications and commercialization in the field.

Synergetic effect in rolling GaIn alloy droplets enables ultralow temperature growth of silicon nanowires at 70 °C on plastics

Ultralow temperature growth of silicon nanowires (SiNWs) directly upon cheap plastics is highly desirable for building high performance soft logics and sensors based on mature Si technology. In this work, a low temperature growth of SiNWs at only 70 °C has been demonstrated for the first time, upon polyethylene terephthalate plastics, by using gallium-indium (GaIn) alloy droplets that consume an amorphous Si (a-Si) layer as the precursor. The GaIn alloy droplets enable a beneficial synergetic effect that helps not only to reduce the melting temperature, but also to install a protective Gibbs adsorption layer of In atoms, which are critical to stabilize the rolling catalyst droplet, against otherwise rapid diffusion loss of Ga into the a-Si matrix. Ultra-long SiNWs can be batch-produced with a precise location and preferred elastic geometry, which paves the way for large scale integration. At <70 °C, a transition from rolling to sprawling dynamics is observed by in situ scanning electron microscopy, caused by reduced diffusion transport and rapid formation of discrete nuclei in the alloy droplet, which provides the basis for continuous growth of SiNWs. This unique capability and critical new understanding open the way for integrating high quality c-Si electronics directly over flexible, lightweight and extremely low cost plastics.

"Optical tentacle" of suspended polymer micro-rings on a multicore fiber facet for vapor sensing

We designed a new type of gas sensor, an optical tentacle, made of highly integrated polymer micro-ring resonators in three-dimensional space on the tiny end-facet of a multicore optical fiber. Two pairs of three polymer micro-ring resonators were hung symmetrically on both sides of three suspended micro-waveguides as the sensing units. The micro-waveguides interlace to form a three-layer nested configuration, which makes the multicore optical fiber a "tentacle" for vapors of volatile organic compounds. Both experiments and theoretical simulation confirmed that the symmetrical coupling of multiple pairs of rings with the micro-waveguide had better resonance than the single ring setup. This is because the symmetrical light modes in the waveguides couple with the rings separately. All the optical micro-components were fabricated by the two-photon lithography technology on the end facet of multicore optical fiber. The optical tentacle shows good sensitivity and reversibility. This approach can also be adopted for sensor array design on a chip. Furthermore, optical sensors that can sense vapors with multiple constituents may be achieved in the future by adding selective sensitive materials to or on the surface of the rings.

Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine

Artificial intelligence (AI) and nanotechnology are two fields that are instrumental in realizing the goal of precision medicine-tailoring the best treatment for each cancer patient. Recent conversion between these two fields is enabling better patient data acquisition and improved design of nanomaterials for precision cancer medicine. Diagnostic nanomaterials are used to assemble a patient-specific disease profile, which is then leveraged, through a set of therapeutic nanotechnologies, to improve the treatment outcome. However, high intratumor and interpatient heterogeneities make the rational design of diagnostic and therapeutic platforms, and analysis of their output, extremely difficult. Integration of AI approaches can bridge this gap, using pattern analysis and classification algorithms for improved diagnostic and therapeutic accuracy. Nanomedicine design also benefits from the application of AI, by optimizing material properties according to predicted interactions with the target drug, biological fluids, immune system, vasculature, and cell membranes, all affecting therapeutic efficacy. Here, fundamental concepts in AI are described and the contributions and promise of nanotechnology coupled with AI to the future of precision cancer medicine are reviewed.

Deep Learning Meets Nanophotonics: A Generalized Accurate Predictor for Near Fields and Far Fields of Arbitrary 3D Nanostructures

Deep artificial neural networks are powerful tools with many possible applications in nanophotonics. Here, we demonstrate how a deep neural network can be used as a fast, general purpose predictor of the full near-field and far-field response of plasmonic and dielectric nanostructures. A trained neural network is shown to infer the internal fields of arbitrary three-dimensional nanostructures many orders of magnitude faster compared to conventional numerical simulations. Secondary physical quantities are derived from the deep learning predictions and faithfully reproduce a wide variety of physical effects without requiring specific training. We discuss the strengths and limitations of the neural network approach using a number of model studies of single particles and their near-field interactions. Our approach paves the way for fast, yet universal, methods for design and analysis of nanophotonic systems.

Piezophototronic gated optofluidic logic computations empowering intrinsic reconfigurable switches

Optofluidic nano/microsystems have advanced the realization of Boolean circuits, with drastic progression to achieve extensive scale integration of desirable optoelectronics to investigate multiple logic switches. In this context, we demonstrate the optofluidic logic operations with interfacial piezophototronic effect to promote multiple operations of electronic analogues. We report an optofluidic Y-channeled logic device with tunable metal-semiconductor-metal interfaces through mechanically induced strain elements. We investigate the configuration of an OR gate in a semiconductor-piezoelectric zinc oxide nanorod-manipulated optofluidic sensor, and its direct reconfiguration to logic AND through compressive strain-induced (−1%) piezoelectric negative polarizations. The exhibited strategy in optofluidic systems implemented with piezophototronic concept enables direct-on chip working of OR and AND logic with switchable photocurrent under identical analyte. Featured smart intrinsic switching between the Boolean optoelectronic gates (OR↔AND) ultimately reduces the need for cascaded logic circuits to operate multiple logic switches on-a-chip.

Designing optofluidic nano/microsystems to realize large-scale Boolean circuits remains a challenge. Here, the authors propose a flexible optofluidic framework to perform binary computations with an integrated piezophototronic mechanism controlling the optofluidic switching of logic gates (PPOF).

Advanced radial junction thin film photovoltaics and detectors built on standing silicon nanowires

Three-dimensional (3D) construction of radial junction hydrogenated amorphous silicon (a-Si:H) thin film solar cells on standing silicon nanowires (SiNWs) is a promising strategy to maximize the light harvesting performance and improve the photocarrier collection in an optimized junction configuration. The unique light in-coupling and absorption behaviour in the antenna-like 3D photonic structures also necessitates a set of new theoretical models and simulation tools to design, predict and optimize the photovoltaic performance of radial junction solar cells, which can be rather different from planar junction solar cells. Recently, the performance of radial junction a-Si:H thin film solar cells has progressed steadily to a level comparable or even superior to that of their planar counterparts, with plenty of room for further improvement. This review will first address the growth strategy and critical parameter control of SiNWs produced via a plasma-assisted low-temperature vapour-liquid-solid procedure using low-melting-point metals as the catalyst. Then, the construction of high-performance radial junction thin film solar cells over the standing SiNW matrix, as well as their optimal structural designs, will be introduced. At the end, the new applications of 3D radial junction units will be summarized, which include, for example, the construction of very flexible, low-cost and efficient a-Si:H solar cells with the highest power-to-weight ratio, the demonstration of highly sensitive solar-blind photodetectors operating at the ultraviolet wavelength spectrum and the development of novel biomimetic radial tandem junction photodetectors with an intrinsic red-green-blue (RGB) colour distinguishing capability.

Detection and Discrimination of Volatile Organic Compounds using a Single Film Bulk Acoustic Wave Resonator with Temperature Modulation as a Multiparameter Virtual Sensor Array

This paper describes the detection and discrimination of volatile organic compounds (VOCs) using an e-nose system based on a multiparameter virtual sensor array (VSA), which consists of a single-chip temperature-compensated film bulk acoustic wave resonator (TC-FBAR) coated with 20-bilayer self-assembled poly(sodium 4-styrenesulfonate)/poly(diallyldimethylammonium chloride) thin films. The high-frequency and microscale FBAR multiparameter VSA was realized by temperature modulation, which can greatly reduce the cost and complexity compared to those of a traditional e-nose system and can allow it to operate at different temperatures. The discrimination effect depends on the synergy of temperature modulation and the sensing material. For proof-of-concept validation purposes, the TC-FBAR was exposed to six different VOC vapors at six different gas partial pressures by real-time VOC static detection and dynamic detection. The resulting frequency shifts and impedance responses were measured at different temperatures and evaluated using principal component analysis and linear discriminant analysis, which revealed that all analytes can be distinguished and classified with more than 97% accuracy. To the best of our knowledge, this report is the first on an FBAR multiparameter VSA based on temperature modulation, and the proposed novel VSA shows great potential as a compact and promising e-nose system integrated in commercial electronic products.

Device Noise Reduction for Silicon Nanowire Field-Effect-Transistor Based Sensors by Using a Schottky Junction Gate

The sensitivity of metal oxide semiconductor field-effect transistor (MOSFET) based nanoscale sensors is ultimately limited by noise induced by carrier trapping/detrapping processes at the gate oxide/semiconductor interfaces. We have designed a Schottky junction gated silicon nanowire field-effect transistor (SiNW-SJGFET) sensor, where the Schottky junction replaces the noisy oxide/semiconductor interface. Our sensor exhibits significantly reduced device noise, 2.1 × 10^-9 V² μm²/Hz at 1 Hz, compared to reference devices with the oxide/semiconductor interface operated at both inversion and depletion modes. Further improvement can be anticipated by wrapping the nanowire by such a Schottky junction, thereby eliminating all oxide/semiconductor interfaces. Hence, a combination of the low-noise SiNW-SJGFET device with a sensing surface of the Nernstian response limit holds promises for future high signal-to-noise ratio sensor applications.