Ultra-Low-Power E-Nose System Based on Multi-Micro-LED-Integrated, Nanostructured Gas Sensors and Deep Learning

Ultralow-Power Single-Sensor-Based E-Nose System Powered by Duty Cycling and Deep Learning for Real-Time Gas Identification

This study presents a novel, ultralow-power single-sensor-based electronic nose (e-nose) system for real-time gas identification, distinguishing itself from conventional sensor-array-based e-nose systems, whose power consumption and cost increase with the number of sensors. Our system employs a single metal oxide semiconductor (MOS) sensor built on a suspended 1D nanoheater, driven by duty cycling─characterized by repeated pulsed power inputs. The sensor's ultrafast thermal response, enabled by its small size, effectively decouples the effects of temperature and surface charge exchange on the MOS nanomaterial's conductivity. This provides distinct sensing signals that alternate between responses coupled with and decoupled from the thermally enhanced conductivity, all within a single time domain during duty cycling. The magnitude and ratio of these dual responses vary depending on the gas type and concentration, facilitating the early stage gas identification of five gas types within 30 s via a convolutional neural network (classification accuracy = 93.9%, concentration regression error = 19.8%). Additionally, the duty-cycling mode significantly reduces power consumption by up to 90%, lowering it to 160 μW to heat the sensor to 250 °C. Manufactured using only wafer-level batch microfabrication processes, this innovative e-nose system promises the facile implementation of battery-driven, long-term, and cost-effective IoT monitoring systems.

Fast-Response and Low-Power Self-Heating Gas Sensor Using Metal/Metal Oxide/Metal (MMOM) Structured Nanowires

With the escalating global awareness of air quality management, the need for continuous and reliable monitoring of toxic gases by using low-power operating systems has become increasingly important. One of which, semiconductor metal oxide gas sensors have received great attention due to their high/fast response and simple working mechanism. More specifically, self-heating metal oxide gas sensors, wherein direct thermal activation in the sensing material, have been sought for their low power-consuming characteristics. However, previous works have neglected to address the temperature distribution within the sensing material, resulting in inefficient gas response and prolonged response/recovery times, particularly due to the low-temperature regions. Here, we present a unique metal/metal oxide/metal (MMOM) nanowire architecture that conductively confines heat to the sensing material, achieving high uniformity in the temperature distribution. The proposed structure enables uniform thermal activation within the sensing material, allowing the sensor to efficiently react with the toxic gas. As a result, the proposed MMOM gas sensor showed significantly enhanced gas response (from 6.7 to 20.1% at 30 ppm), response time (from 195 to 17 s at 30 ppm), and limit of detection (∼1 ppm) when compared to those of conventional single-material structures upon exposure to carbon monoxide. Furthermore, the proposed work demonstrated low power consumption (2.36 mW) and high thermal durability (1500 on/off cycles), demonstrating its potential for practical applications in reliable and low-power operating gas sensor systems. These results propose a new paradigm for power-efficient and robust self-heating metal oxide gas sensors with potential implications for other fields requiring thermal engineering.

A Temperature Prediction Model for Flexible Electronic Devices Based on GA-BP Neural Network and Experimental Verification

The problem that the thermal safety of flexible electronic devices is difficult to evaluate in real time is addressed in this study by establishing a BP neural network (GA-BPNN) temperature prediction model based on genetic algorithm optimisation. The model uses a BP neural network to fit the functional relationship between the input condition and the steady-state temperature of the equipment and uses a genetic algorithm to optimise the parameter initialisation problem of the BP neural network. To overcome the challenge of the high cost of obtaining experimental data, finite element analysis software is used to simulate the temperature results of the equipment under different working conditions. The prediction variance of the GA-BPNN model does not exceed 0.57 °C and has good robustness, as the model is trained according to the simulation data. The study conducted thermal validation experiments on the temperature prediction model for this flexible electronic device. The device reached steady state after 1200 s of operation at rated power. The error between the predicted and experimental results was less than 0.9 °C, verifying the validity of the model's predictions. Compared with traditional thermal simulation and experimental methods, this model can quickly predict the temperature with a certain accuracy and has outstanding advantages in computational efficiency and integrated application of hardware and software.

Ultrasensitive Online NO Sensor Based on a Distributed Parallel Self-Regulating Neural Network and Ultraviolet Differential Optical Absorption Spectroscopy for Exhaled Breath Diagnosis

The concentration of fractional exhaled nitric oxide (FeNO) is closely related to human respiratory inflammation, and the detection of its concentration plays a key role in aiding diagnosing inflammatory airway diseases. In this paper, we report a gas sensor system based on a distributed parallel self-regulating neural network (DPSRNN) model combined with ultraviolet differential optical absorption spectroscopy for detecting ppb-level FeNO concentrations. The noise signals in the spectrum are eliminated by discrete wavelet transform. The DPSRNN model is then built based on the separated multipeak characteristic absorption structure of the UV absorption spectrum of NO. Furthermore, a distributed parallel network structure is built based on each absorption feature region, which is given self-regulating weights and finally trained by a unified model structure. The final self-regulating weights obtained by the model indicate that each absorption feature region contributes a different weight to the concentration prediction. Compared with the regular convolutional neural network model structure, the proposed model has better performance by considering the effect of separated characteristic absorptions in the spectrum on the concentration and breaking the habit of bringing the spectrum as a whole into the model training in previous related studies. Lab-based results show that the sensor system can stably achieve high-precision detection of NO (2.59-750.66 ppb) with a mean absolute error of 0.17 ppb and a measurement accuracy of 0.84%, which is the best result to date. More interestingly, the proposed sensor system is capable of achieving high-precision online detection of FeNO, as confirmed by the exhaled breath analysis.

Gas Graph Convolutional Transformer for Robust Generalization in Adaptive Gas Mixture Concentration Estimation

Gas concentration estimation has a tremendous research significance in various fields. However, existing methods for estimating the concentration of mixed gases generally depend on specific data-preprocessing methods and suffer from poor generalizability to diverse types of gases. This paper proposes a graph neural network-based gas graph convolutional transformer model (GGCT) incorporating the information propagation properties and the physical characteristics of temporal sensor data. GGCT accurately predicts mixed gas concentrations and enhances its generalizability by analyzing the concentration tokens. The experimental results highlight the GGCT's robust performance, achieving exceptional levels of accuracy across most tested gas components, underscoring its strong potential for practical applications in mixed gas analysis.

Dual-Photosensitizer Synergy Empowers Ambient Light Photoactivation of Indium Oxide for High-Performance NO2 Sensing

Light-activated chemiresistors offer a powerful approach to achieving lower-temperature gas sensing with unprecedented sensitivities. However, an incomplete understanding of how photoexcited charge carriers enhance sensitivity obstructs the rational design of high-performance sensors, impeding the practical utilization under commonly accessible light sources instead of ultraviolet or higher-energy sources. Here, a rational approach is presented to modulate the electronic properties of the parent metal oxide phase, exemplified by this model system of Bi-doped In2 O3 nanofibers decorated with Au nanoparticles (NPs) that exhibit superior NO2 sensing performance. Bi doping introduces mid-gap energy levels into In2 O3 , promoting photoactivation even under visible blue light. Additionally, green-absorbing plasmonic Au NPs facilitate electron transfer across the heterojunction, extending the photoactive region toward the green light. It is revealed that the direct involvement of photogenerated charge carriers in gas adsorption and desorption processes is pivotal for enhancing gas sensing performance. Owing to the synergistic interplay between the Bi dopants and the Au NPs, the Au-Bix In2-x O3 (x = 0.04) sensing layers attain impressive response values (Rg /Ra  = 104 at 0.6 ppm NO2 ) under green light illumination and demonstrate practical viability through evaluation under simulated mixed-light conditions, all of which significantly outperforms previously reported visible light-activated NO2 sensors.

Prompt Electronic Discrimination of Gas Molecules by Self-Heating Temperature Modulation

Though considerable progress has been achieved on gas molecule recognition by electronic nose (e-nose) comprised of nonselective (metal oxide) semiconductor chemiresistors, extracting adequate molecular features within short time (<1 s) remains a big obstacle, which hinders the emerging e-nose applications in lethal or explosive gas warning. Herein, by virtue of the ultrafast (∼20 μs) thermal relaxation time of self-heated WO3-based chemiresistors fabricated via oblique angle deposition, instead of external heating, self-heating temperature modulation has been proposed to generate sufficient electrical response features. Accurate discrimination of 12 gases (including 3 xylene isomers with the same function group and molecular weight) has been readily achieved within 0.5-1 s, which is one order faster than the state-of-the-art e-noses. A smart wireless e-nose, capable of instantaneously discriminating target gas in ambient air background, has been developed, paving the way for the practical applications of e-nose in the area of homeland security and public health.

Flicker Noise in Resistive Gas Sensors-Measurement Setups and Applications for Enhanced Gas Sensing

We discuss the implementation challenges of gas sensing systems based on low-frequency noise measurements on chemoresistive sensors. Resistance fluctuations in various gas sensing materials, in a frequency range typically up to a few kHz, can enhance gas sensing by considering its intensity and the slope of power spectral density. The issues of low-frequency noise measurements in resistive gas sensors, specifically in two-dimensional materials exhibiting gas-sensing properties, are considered. We present measurement setups and noise-processing methods for gas detection. The chemoresistive sensors show various DC resistances requiring different flicker noise measurement approaches. Separate noise measurement setups are used for resistances up to a few hundred kΩ and for resistances with much higher values. Noise measurements in highly resistive materials (e.g., MoS2, WS2, and ZrS3) are prone to external interferences but can be modulated using temperature or light irradiation for enhanced sensing. Therefore, such materials are of considerable interest for gas sensing.

Recent Advances in Sensor-Actuator Hybrid Soft Systems: Core Advantages, Intelligent Applications, and Future Perspectives

The growing demand for soft intelligent systems, which have the potential to be used in a variety of fields such as wearable technology and human-robot interaction systems, has spurred the development of advanced soft transducers. Among soft systems, sensor-actuator hybrid systems are considered the most promising due to their effective and efficient performance, resulting from the synergistic and complementary interaction between their sensor and actuator components. Recent research on integrated sensor and actuator systems has resulted in a range of conceptual and practical soft systems. This review article provides a comprehensive analysis of recent advances in sensor and actuator integrated systems, which are grouped into three categories based on their primary functions: i) actuator-assisted sensors for intelligent detection, ii) sensor-assisted actuators for intelligent movement, and iii) sensor-actuator interactive devices for a hybrid of intelligent detection and movement. In addition, several bottlenecks in current studies are discussed, and prospective outlooks, including potential applications, are presented. This categorization and analysis will pave the way for the advancement and commercialization of sensor and actuator-integrated systems.

Low-cost electronic-nose (LC-e-nose) systems for the evaluation of plantation and fruit crops: recent advances and future trends

An electronic nose (e-nose) is a device designed to recognize and classify odors. The equipment is built around a series of sensors that detect the presence of odors, especially volatile organic compounds (VOCs), and generate an electric signal (voltage), known as e-nose data, which contains chemical information. In the food business, the use of e-noses for analyses and quality control of fruits and plantation crops has increased in recent years. Their use is particularly relevant due to the lack of non-invasive and inexpensive methods to detect VOCs in crops. However, the majority of reports in the literature involve commercial e-noses, with only a few studies addressing low-cost e-nose (LC-e-nose) devices or providing a data-oriented description to assist researchers in choosing their setup and appropriate statistical methods to analyze crop data. Therefore, the objective of this study is to discuss the hardware of the two most common e-nose sensors: electrochemical (EC) sensors and metal oxide sensors (MOSs), as well as a critical review of the literature reporting MOS-based low-cost e-nose devices used for investigating plantations and fruit crops, including the main features of such devices. Miniaturization of equipment from lab-scale to portable and convenient gear, allowing producers to take it into the field, as shown in many appraised systems, is one of the future advancements in this area. By utilizing the low-cost designs provided in this review, researchers can develop their own devices based on practical demands such as quality control and compare results with those reported in the literature. Overall, this review thoroughly discusses the applications of low-cost e-noses based on MOSs for fruits, tea, and coffee, as well as the key features of their equipment (i.e., advantages and disadvantages) based on their technical parameters (i.e., electronic and physical parts). As a final remark, LC-e-nose technology deserves significant attention as it has the potential to be a valuable quality control tool for emerging countries.

Target-Driven Z-Scheme Heterojunction Formation for ppb H2S Detection from Exhaled Breath at Room Temperature

As a biomarker of periodontitis, sensitive and timely monitoring of hydrogen sulfide (H2S) in exhaled breath at room temperature (RT) is important for the early intervention of oral diseases. However, the required high operation temperature to achieve high sensitivity is still a technical challenge for directly monitoring exhaled breath. In this study, by integrating metal-organic frameworks (MOFs) into self-aligned TiO2 nanotube arrays (NTs), a chemiresistor gas sensor with outstanding sensitivity and selectivity was constructed for the detection of H2S at RT. The precise regulation of a Co(III)-based MOF CoBDC-NH2 (BDC-NH2 = 2-aminoterephthalic acid) not only induced more active surface for the preconcentration of the target gas but also caused a buildup of Z-scheme heterojunctions in the H2S atmosphere that induced an ultrahigh sensitivity at RT via 365 nm light-emitting diode irradiation. The response and recovery times decreased to ∼50 and ∼28%, respectively, when this system was exposed to UV light. The sensing chips based on the as-prepared TiO2/CoBDC-NH2 NTs exhibited the highest-ranking H2S sensing performance, i.e., a limit of detection of 1.3 ppb and excellent selectivity even to 100 times high concentration of interference gases, owing to the synergistic chemical environment provided by NH2-functionalized Co-MOFs and abundant photogenerated electrons provided by Z-scheme heterojunctions. This sensing chip was also used in a practical application for the timely monitoring of halitosis from direct exhaled breath. This study provides a reliable and sensitive design for clinically aiding the timely detection of H2S in a complex oral environment.

A polylactide based multifunctional hydrophobic film for tracking evaluation and maintaining beef freshness by an electrospinning technique

A nanofiber film was prepared by a facile electrospinning technique using polylactide (PLA), butterfly pea flower extract (BPA) and cinnamaldehyde (CIN). The as-prepared film shows the prominent antioxidative, antibacterial, colorimetric and hydrophobic properties so that the beef freshness can be monitored and maintained up to 6 days at 4 °C simultaneously. Besides, the nanofiber structure endows the film with a fast color responsiveness under acidic-alkaline atmospheres with different concentrations. Moreover, this film exhibits higher tensile strength (9.56 Mpa) than that of the pure PLA electrospinning film (4.40 Mpa). Especially the introduction of the BPA effectively boosts the antimicrobial ability of the CIN. The freshness, sub-freshness and spoilage levels of the beef can be easily testified by observing the color difference change of the film. So the polylactide based multifunctional film as an intelligent packaging has an excellent potential for the sub-freshness detection of meat.

MicroLED/LED electro-optical integration techniques for non-display applications

MicroLEDs offer an extraordinary combination of high luminance, high energy efficiency, low cost, and long lifetime. These characteristics are highly desirable in various applications, but their usage has, to date, been primarily focused toward next-generation display technologies. Applications of microLEDs in other technologies, such as projector systems, computational imaging, communication systems, or neural stimulation, have been limited. In non-display applications which use microLEDs as light sources, modifications in key electrical and optical characteristics such as external efficiency, output beam shape, modulation bandwidth, light output power, and emission wavelengths are often needed for optimum performance. A number of advanced fabrication and processing techniques have been used to achieve these electro-optical characteristics in microLEDs. In this article, we review the non-display application areas of the microLEDs, the distinct opto-electrical characteristics required for these applications, and techniques that integrate the optical and electrical components on the microLEDs to improve system-level efficacy and performance.