Jianhua Yang

Developing low‐cost and highly efficient S‐scheme heterojunction photocatalysts is still a significant challenge towards enhancing the activity of photocatalytic hydrogen evolution (PHE). This study created an S‐scheme heterojunction by in situ growing inorganic Al‐doped SrTiO ₃ (ASTO), which possesses superior oxidation capability, on a substrate of the covalent organic framework (TpPa‐1‐COF), which has great reduction capacity, using a solvothermal method. With the advantages of stronger redox capacity, quicker electron transport, and more potent carrier separation, the optimal 5% ASTO/TpPa‐1 S‐scheme heterojunction achieved remarkable photocatalytic performance in ascorbic acid (AsA) solution when exposed to simulated solar light, with a hydrogen production rate of 4.12 mmol g ⁻¹ h ⁻¹ , which is 14.2 and 11.4 times higher than that of pure TpPa‐1 and ASTO, respectively. This performance outperforms most recently reported SrTiO ₃ ‐based and TpPa‐1‐COF‐based heterojunctions under similar conditions. Notably, an intense interfacial internal electric field (IEF) in ASTO/TpPa‐1 heterojunction was formed resulting from the free electron consumption in TpPa‐1 and accumulation in ASTO, which could speed up the transfer dynamics of photoinduced electrons from the conduction band (CB) of ASTO to the valence band (VB) of TpPa‐1 via an interfacial electron‐transfer channel that follows the directed S‐scheme migration process. Moreover, the direction of the IEF is from TpPa‐1 to ASTO, which could accelerate charge separation and migration, thereby prolonging the lifetimes of charge carriers. The dynamic behavior of photoinduced carriers was confirmed by femtosecond transient absorption spectroscopy (fs‐TAS). Overall, this study provides valuable guidance for the rational design of an innovative organic/inorganic hybrid S‐scheme heterojunction.

The selection of the sensor array represents a pivotal aspect of the system design for the electronic nose (E-nose). In practical applications, achieving an optimal balance between array size and system performance is often challenging. Therefore, realizing a high-performance E-nose with a minimum number of sensors is necessary, particularly for portable E-noses with limited size and power. This study proposes a cost-effectiveness ratio (CER) as an array optimization criterion to address these issues. The CER is defined for quantifying costs and benefits as a basis for array optimization. Applying the designed array optimization criterion to the portable E-nose system, which comprises 8 MEMS sensors, achieves an 80% prediction accuracy while reducing the number of sensors by nearly 40%. In addition, the concept of extreme sensor number is proposed to illustrate the existence of limit values for the number of sensors in the process of array optimization. This study offers a foundation for quantitative metrics for sensor array optimization, which serves as a crucial reference for the design of size- and power-sensitive portable E-nose systems.

Real-time and accurate biomarker detection is highly desired in point-of-care diagnosis, food freshness monitoring, and hazardous leakage warning. However, achieving such an objective with existing technologies is still challenging. Herein, we demonstrate a wireless inductor-capacitor (LC) chemical sensor based on platinum-doped partially deprotonated-polypyrrole (Pt-PPy⁺ and PPy⁰) for real-time and accurate ammonia (NH₃) detection. With the chemically wide-range tunability of PPy in conductivity to modulate the impedance, the LC sensor exhibits an up-to-180% improvement in return loss (S11). The Pt-PPy⁺ and PPy⁰ shows the p-type semiconductor nature with greatly-manifested adsorption-charge transfer dynamics toward NH₃, leading to an unprecedented NH₃ sensing range. The S11 and frequency of the Pt-PPy⁺ and PPy⁰-based sensor exhibit discriminative response behaviors to humidity and NH₃, enabling the without-external-calibration compensation and accurate NH₃ detection. A portable system combining the proposed wireless chemical sensor and a handheld instrument is validated, which aids in rationalizing strategies for individuals toward various scenarios.

Flexible ammonia (NH₃) gas sensors have gained increasing attention for their potential in medical diagnostics and health monitoring, as they serve as a biomarker for kidney disease. Utilizing the pre-designable and porous properties of covalent organic frameworks (COFs) is an innovative way to address the demand for high-performance NH₃ sensing. However, COF particles frequently encounter aggregation, low conductivity, and mechanical rigidity, reducing the effectiveness of portable NH₃ detection. To overcome these challenges, we propose a practical approach using polyvinyl alcohol-carrageenan (κPVA) as a template for in the situ growth of two-dimensional COF film and particles to produce a flexible hydrogel gas sensor (COF/κPVA). The synergistic effect of COF and κPVA enhances the gas sensing, water retention, and mechanical properties. The COF/κPVA hydrogel shows a 54.4% response to 1 ppm NH₃ with a root mean square error of less than 5% and full recovery compared to the low response and no recovery of bare κPVA. Owing to the dual effects of the COF film and the particles anchoring the water molecules, the COF/κPVA hydrogel remained stable after 70 h in atmospheric conditions, in contrast, the bare κPVA hydrogel was completely dehydrated. Our work might pave the way for highly sensitive hydrogel gas sensors, which have intriguing applications in flexible electronic devices for gas sensing.

MoTe₂/InN vdW heterostructure in AA stacking has a type-II band alignment and is proven to be ideal for NO₂ detection via resistive and optical gas sensors.

The electronic nose (E-nose) is widely used for quantitative monitoring of poisonous and harmful gases. However, its conventional gas sensing is generally carried out based on a single estimator which lacks a large-scale training set to train the models, leading to a low classification and regression accuracy. In this regard, this work explores an E-nose based on a gas sensor array, which predicts both types and concentrations of gases accurately and efficiently. Classical ensemble machine learning algorithms, XGBoost, and random forest, are selected as candidates for designing the classifiers and regressors of the E-nose. The classifier recognizes six different volatile organic compounds (VOCs), while the regressor predicts the concentration of each gas component. The 5-fold cross-validation method is used to search for the best training parameters of each model. Different from the previous works that solely relied on a single model, such as back-propagation neural network and decision tree, this work constructs a model system based on XGBoost-random forest algorithms, which demonstrates superior performance. The model system achieves an accuracy of 96.0% using the XGBoost classifier and an average R ² score of 0.923 using the random forest regressor in predicting six kinds of VOCs. Moreover, the novel model system has a high testing efficiency, with predicting time of 0.011 s in the classification process and 0.015 s in the regression process. This study provides a novel approach to designing an accurate and efficient E-nose system for the quantitative detection of VOCs.

Two-dimensional (2D) phthalocyanine-based covalent organic frameworks (COFs) provide an ideal platform for efficient and rapid gas sensing—this can be attributed to their regular structure, moderate conductivity, and a large number of scalable metal active centers. However, there remains a need to explore structural modification strategies for optimizing the sluggish desorption process caused by the extensive porosity and strong adsorption effect of metal sites. Herein, we reported a 2D bimetallic phthalocyanine-based COF (COF-CuNiPc) as chemiresistive gas sensors that exhibited a high gas-sensing performance to nitrogen dioxide (NO₂). Bimetallic COF-CuNiPc with an asymmetric synergistic effect achieves a fast adsorption/desorption process to NO₂. It is demonstrated that the COF-CuNiPc can detect 50 ppb NO₂ with a recovery time of 7 s assisted by ultraviolet illumination. Compared with single-metal phthalocyanine-based COFs (COF-CuPc and COF-NiPc), the bimetallic structure of COF-CuNiPc can provide a proper band gap to interact with NO₂ gas molecules. The CuNiPc heterometallic active site expands the overlap of d-orbitals, and the optimized electronic arrangement accelerates the adsorption/desorption processes. The concept of a synergistic effect enabled by bimetallic phthalocyanines in this work can provide an innovative direction to design high-performance chemiresistive gas sensors.

This work presents an electronic nose-based gas identification and concentration estimation method for detection of food spoilage. The response data of sensors were acquired through a commercial gas sensor array and data acquisition circuit board, and transformed into pictures with the form of the Fourier series. A convolutional neural network model was used to identify the pictures from the conversion of sensor data, thus achieving the purpose of identifying the gases (C ₂ H ₅ OH, NH ₃ and H ₂ S). In order to solve the problem of sample imbalance and to improve the generalization performance of classification models, the synthetic minority oversampling technique and dropout technique were employed. Five-fold cross-validation was used to evaluate the performance of the model, of which the gas identification accuracy rate reached 96.67%. Moreover, a gas concentration regression model with the advantages of simplicity and strong interpretability was further proposed to estimate the concentrations of C ₂ H ₅ OH, NH ₃ and H ₂ S. The mean absolute errors and coefficients of determination for the concentration estimation of C ₂ H ₅ OH, NH ₃ and H ₂ S are (3.71 ppm, 0.968), (0.50 ppm, 0.968) and (0.13 ppm, 0.99), respectively. Furthermore, we used our model to evaluate the freshness of kiwifruit, pork and beef, and it showed satisfactory predictive performance. The method proposed in this work realizes high-precision detection of gases from food spoilage, and has a good application prospect in the rapid judgment of food freshness on the electronic nose system.

An electronic nose (E-nose) based on three metal oxide semiconductor (MOS) gas sensors is designed to quantitatively analyse six types of volatile organic compounds (VOCs). Support vector machine (SVM), extreme learning machine (ELM), and back-propagation (BP) neural network, are used to design different classifiers and regressors for integrating a suitable pattern recognition model of the E-nose system. By using the output of the classifier as one of the input features of the regressor, the models can predict the concentration of different types of VOCs at the same time. The 5-fold cross-validation is applied to search optimal parameters of each model and the independent test is conducted to evaluate the generalization performance. A pipeline is used to connect the best classifier and the best regressor, constructing an integrated model for pattern recognition. The integrated model based on ELM-ELM structure exhibits the best performance. Classification accuracy can be as high as 99% and the R ² score of regression as high as 0.97 in the 5-fold cross-validation. Classification accuracy is up to 93% and the R ² score of regression is up to 0.94 in the independent tests. Furthermore, the integrated model has prominent training and test efficiency, of which time consumptions are both shorter than 0.1 s. This work provides an approach to design an efficient integrated model with high performance for gas type identification and concentration prediction in the E-nose system.