Machine Learning-Assisted Sensor Based on CsPbBr3@ZnO Nanocrystals for Identifying Methanol in Mixed Environments

In situ fabrication of porous polymer films embedded with perovskite nanocrystals for flexible superhydrophobic piezoresistive sensors

The application of pressure sensors based on perovskite in high-humidity environments is limited by the effect of water on their stability. Endowing sensors with superhydrophobicity is an effective strategy to overcome the issue. In this work, MAPbBr3/Polyvinylidene Fluoride-TFSI composite was prepared by a one-step in-situ strategy to form a flexible superhydrophobic pressure sensor, which exhibited a contact angle of 150.25°. The obtained sensor exhibited a sensitivity of 0.916 in 1 kPa, a detection limit of 0.2 Pa, a precision of 0.1 Pa, and a response/recovery of ∼100 ms, along with good thermal stability. Through density functional theory calculations, it is revealed that the formation of the porosity is attributed to the interaction between the polymer and EMIM TFSI, which further leads to superhydrophobicity. And, the perovskite structure is easy to change under pressure, affecting the carrier transport and electrical signals output, which explains the sensing mechanism. In addition, the sensor performed well in monitoring facial expression, pulse, respiration, finger bending, and wind speed ranging from 1 m/s to 6 m/s. With both the Linear Regression and the Random Forest algorithm, the sensor can monitor the wind speed with an R2 greater than 0.977 in 60 tests.

Ultrafast Visual Detection of a Trace Amount of Water by Highly Efficient Hybrid Manganese Halides

A quantitative water detection method is urgently needed in storage facilities, space exploration, and the chemical industry. Although numerous physical techniques have been widely utilized to determine the water content, they still suffer from many disadvantages such as highly expensive special instruments, complicated analysis processes, etc. Hence, a convenient, rapid, and sensitive water analysis method is highly desirable. Herein, we developed a visual fluorescence sensing technology for water detection based on reversible PL off-on switching of organic-inorganic hybrid zero-dimensional (0D) manganese halides. In this work, a family of hybrid manganese halides were synthesized through a facile solution method, namely, [NH4(18-Crown-6)]2MnBr4, [Ca(18-Crown-6)·3H2O](18-Crown-6)MnBr4, [NH4(dibenzo-18-Crown-6)]2MnBr4, and [Ca(dibenzo-18-Crown-6)·2H2O]MnBr4. Excited by UV light, these highly crystalline manganese halides exhibit strong green light emissions from the d-d electron transition of Mn2+ with near-unity photoluminescence quantum yield and submillisecond lifetime. Benefiting from the dynamic and weak ionic bonding interactions, these 0D manganese halides display reversible water-response on/off luminescence switching but fail in any other aprotic solvents. Therefore, these 0D hybrid manganese halides can be explored as ultrafast visual fluorescence probes to detect the trace amount of water in organic solvents with multiple superiorities of rapid response time (< 2 s), ultralow detection limit (9.71 ppm), excellent repeatability, etc. The reversible water-response luminescent on/off switching also provides a binary optical gate with advanced applications in anticounterfeiting and information security, etc.

General Model for Predicting Response of Gas-Sensitive Materials to Target Gas Based on Machine Learning

Gas sensors play a crucial role in various industries and applications. In recent years, there has been an increasing demand for gas sensors in society. However, the current method for screening gas-sensitive materials is time-, energy-, and cost-consuming. Consequently, an imperative exists to enhance the screening efficiency. In this study, we proposed a collaborative screening strategy through integration of density functional theory and machine learning. Taking zinc oxide (ZnO) as an example, the responsiveness of ZnO to the target gas was determined quickly on the basis of the changes in the electronic state and structure before and after gas adsorption. In this work, the adsorption energy and electronic and structural characteristics of ZnO after adsorbing 24 kinds of gases were calculated. These computed features served as the basis for training a machine learning model. Subsequently, various machine learning and evaluation algorithms were utilized to train the fast screening model. The importance of feature values was evaluated by the AdaBoost, Random Forest, and Extra Trees models. Specifically, charge transfer was assigned importance values of 0.160, 0.127, and 0.122, respectively, ranking as the highest among the 11 features. Following closely was the d-band center, which was presumed to exert influence on electrical conductivity and, consequently, adsorption properties. With 5-fold cross-validation using the Extra Tree accuracy, the 24-sample data set achieved an accuracy of 88%. The 72-sample data set achieved an accuracy of 78% using multilayer perceptron after 5-fold cross-validation, with both data sets exhibiting low standard deviations. This verified the accuracy and reliability of the strategy, showcasing its potential for rapidly screening a material's responsiveness to the target gas.

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time-consuming and labor-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, an analysis of machine learning methods used for studying achiral nanomaterials is provided, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. An overview of chiral nanomaterials within the framework of synthesis-structure-property-application relationships is presented and insights on how to leverage machine learning for the study of these highly complex relationships are provided. Some key recent publications are reviewed and discussed on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.

Room-Temperature Smart Sensor Based on Indium Acetate-Functionalized Perovskite CsPbBr3 Nanocrystals for Monitoring Electrolyte in Lithium-Ion Batteries

Monitoring electrolyte components is an effective means of determining the safety status of lithium-ion batteries. In this study, indium acetate was taken as a ligand to functionalize perovskite CsPbBr3 nanocrystals, and then the room-temperature electrolyte sensor based on CsPbBr3 nanocrystals with ligand indium acetate was prepared. The sensor offers high response, long-term stability (21 days), and low detection limits for ethyl methyl carbonate (10 ppm), diethyl carbonate (10 ppm), and ethyl butyrate (1 ppm) gases at room temperature and boasts a fast response/recovery time (1500 ppm, 58.27/103.82 s, 33.58/40.62 s, and 45.05/103.08 s, respectively). Density functional theory results show that the gas sensitivity comes from the adsorption of an electrolyte, which changes the density-of-state distribution so that the electrical response curve changes. And using computational fluid dynamics simulation, it was found that the time required for gas detection by the built-in sensor (3.1 s) was 8.7 times shorter than that of the implantable sensor. This work provides inspiration and rationale for embedding and integrating room-temperature sensors into lithium-ion batteries to monitor safety and health conditions.

Pattern Recognition with Temperature Regulation: A Single YSZ-Based Mixed Potential Sensor Classifies Multiple Mixtures of Isoprene, n-Propanol, and Acetone

Gas sensors integrated with machine learning algorithms have aroused keen interest in pattern recognition, which ameliorates the drawback of poor selectivity on a sensor. Among various kinds of gas sensors, the yttria-stabilized zirconia (YSZ)-based mixed potential-type sensor possesses advantages of low cost, simple structure, high sensitivity, and superior stability. However, as the number of sensors increases, the increased power consumption and more complicated integration technology may impede their extensive application. Herein, we focus on the development of a single YSZ-based mixed potential sensor from sensing material to machine learning for effective detection and discrimination of unary, binary, and ternary gas mixtures. The sensor that is sensitive to isoprene, n-propanol, and acetone is manufactured with the MgSb2O6 sensing electrode prepared by a simple sol-gel method. Unique response patterns for specific gas mixtures could be generated with temperature regulation. We chose seven algorithm models to be separately trained for discrimination. In order to realize more accurate discrimination, we further discuss the selection of suitable feature parameters and its reasons. With temperature regulation coefficients which are easily available as feature input to model, a single sensor is verified to achieve elevated accuracy rates of 95 and 99% for the discrimination of seven gases (three unary gases, three binary gas mixtures, and one ternary gas mixture) and redefined six gas mixtures. This article provides a potential new approach via a mixed potential sensor instead of a sensor array that could provide a wide application prospect in the field of electronic nose and artificial olfaction.

Wearable Nano-Based Gas Sensors for Environmental Monitoring and Encountered Challenges in Optimization

With a rising emphasis on public safety and quality of life, there is an urgent need to ensure optimal air quality, both indoors and outdoors. Detecting toxic gaseous compounds plays a pivotal role in shaping our sustainable future. This review aims to elucidate the advancements in smart wearable (nano)sensors for monitoring harmful gaseous pollutants, such as ammonia (NH3), nitric oxide (NO), nitrous oxide (N2O), nitrogen dioxide (NO2), carbon monoxide (CO), carbon dioxide (CO2), hydrogen sulfide (H2S), sulfur dioxide (SO2), ozone (O3), hydrocarbons (CxHy), and hydrogen fluoride (HF). Differentiating this review from its predecessors, we shed light on the challenges faced in enhancing sensor performance and offer a deep dive into the evolution of sensing materials, wearable substrates, electrodes, and types of sensors. Noteworthy materials for robust detection systems encompass 2D nanostructures, carbon nanomaterials, conducting polymers, nanohybrids, and metal oxide semiconductors. A dedicated section dissects the significance of circuit integration, miniaturization, real-time sensing, repeatability, reusability, power efficiency, gas-sensitive material deposition, selectivity, sensitivity, stability, and response/recovery time, pinpointing gaps in the current knowledge and offering avenues for further research. To conclude, we provide insights and suggestions for the prospective trajectory of smart wearable nanosensors in addressing the extant challenges.

Multi-Functional Ethylene-vinyl Acetate Copolymer Flexible Composite Film Embedded with Indium Acetate-Passivated Perovskite Quantum Dots

In recent years, all-inorganic cesium lead halide perovskite quantum dots have emerged as promising candidates for various optoelectronic applications, including sensors, light-emitting diodes, and solar cells, owing to their exceptional photoelectric properties. However, their commercial utilization has been limited by stability issues. In this study, we addressed this challenge by passivating the surface defects of CsPbBr3 quantum dots using indium acetate, a metal-organic compound. The resulting CsPbBr3 quantum dots exhibited not only high photoluminescence intensity, but also a remarkably narrow half-peak width of 19 nm. Furthermore, by embedding the CsPbBr3 quantum dots in ethylene-vinyl acetate, we achieved stretchability and significantly enhanced stability while preserving the original luminous intensity. The resulting composite film demonstrated the potential to improve the power conversion efficiency of crystalline silicon solar cells and enabled the creation of excellent white light-emitting diodes with coordinates of (0.33, 0.31). This co-passivation strategy, involving surface passivation and polymer packaging, provides a new idea for the practical application of CsPbBr3 quantum dots.