Highly Sensitive and Selective Triethylamine Sensing through High-Entropy Alloy (Ti-Zr-Cr-V-Ni) Nanoparticle-Induced Fermi Energy Control of MoS2 Nanosheets

High-entropy-based nano-materials for sustainable environmental applications

High entropy materials (HEMs), epitomized by high entropy alloys (HEAs), have sparked immense interest for a range of clean energy and environmental applications due to their remarkable structural versatility and adjustable characteristics. In the face of environmental challenges, HEMs have emerged as valuable tools for addressing issues ranging from wastewater remediation to energy conversion and storage. This review provides a comprehensive exploration of HEMs, spotlighting their catalytic capabilities in diverse redox reactions, such as carbon dioxide reduction to value-added products, degradation of organic pollutants, oxygen reduction, hydrogen evolution, and ammonia decomposition using electrocatalytic and photocatalytic pathways. Additionally, the review highlights HEMs as novel electrode nanomaterials, with the potential to enhance the performance of batteries and supercapacitors. Their unique features, including high capacitance, electrical conductivity, and thermal stability, make them valuable components for meeting crucial energy demands. Furthermore, the review examines challenges and opportunities in advancing HEMs, emphasizing the importance of understanding the underlying mechanisms governing their catalytic and electrochemical behaviors. Essential considerations for optimizing the HEM performance in catalysis and energy storage are outlined to guide future research. Moreover, to provide a comprehensive understanding of the current research landscape, a meticulous bibliometric analysis is presented, offering insights into the trends, focal points, and emerging directions within the realm of HEMs, particularly in addressing environmental concerns.

Quasicrystal Nanosheet/α-Fe2O3 Heterostructure-Based Low Power NO2 Sensors: Experimental and DFT Studies

Industrial emissions, environmental monitoring, and medical fields have put forward huge demands for high-performance and low power consumption sensors. Two-dimensional quasicrystal (2D QC) nanosheets of metallic multicomponent Al70Co10Fe5Ni10Cu5 have emerged as a promising material for gas sensors due to their excellent catalytic and electronic properties. Herein, we demonstrate highly sensitive and selective NO2 sensors developed by low-cost and scalable fabrication techniques using 2D QC nanosheets and α-Fe2O3 nanoparticles. The sensitivity (ΔR/R%) of the optimal amount of 2D QC nanosheet-loaded α-Fe2O3 sensor was 32%, which is significantly larger about 3.5 times than bare α-Fe2O3 sensors for 1 ppm of NO2 at 150 °C operating temperature. The sensors exhibited p-type conduction, and resistance was reduced when exposed to NO2, an oxidizing gas. The enhanced sensing characteristics are a result of the formation of nanoheterojunctions between 2D QC and α-Fe2O3, which improved the charge transport and provided a large sensing signal. In addition, the heterojunction sensor demonstrated excellent NO2 selectivity over other oxidizing and reducing gases. Furthermore, density functional theory calculation examines the adsorption energy and charge transfer between NO2 molecules on the α-Fe2O3(110) and QC/α-Fe2O3(110) heterostructure surfaces, which coincides well with the experimental results.

Metal oxide-based gas sensor array for VOCs determination in complex mixtures using machine learning

Detection of volatile organic compounds (VOCs) from the breath is becoming a viable route for the early detection of diseases non-invasively. This paper presents a sensor array of 3 component metal oxides that give maximal cross-sensitivity and can successfully use machine learning methods to identify four distinct VOCs in a mixture. The metal oxide sensor array comprises NiO-Au (ohmic), CuO-Au (Schottky), and ZnO-Au (Schottky) sensors made by the DC reactive sputtering method and having a film thickness of 80-100 nm. The NiO and CuO films have ultrafine particle sizes of < 50 nm and rough surface texture, while ZnO films consist of nanoscale platelets. This array was subjected to various VOC concentrations, including ethanol, acetone, toluene, and chloroform, one by one and in a pair/mix of gases. Thus, the response values show severe interference and departure from commonly observed power law behavior. The dataset obtained from individual gases and their mixtures were analyzed using multiple machine learning algorithms, such as Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree, Linear Regression, Logistic Regression, Naive Bayes, Linear Discriminant Analysis, Artificial Neural Network, and Support Vector Machine. KNN and RF have shown more than 99% accuracy in classifying different varying chemicals in the gas mixtures. In regression analysis, KNN has delivered the best results with an R2 value of more than 0.99 and LOD of 0.012 ppm, 0.015 ppm, 0.014 ppm, and 0.025 ppm for predicting the concentrations of acetone, toluene, ethanol, and chloroform, respectively, in complex mixtures. Therefore, it is demonstrated that the array utilizing the provided algorithms can classify and predict the concentrations of the four gases simultaneously for disease diagnosis and treatment monitoring.

Functionalized Multiwalled Carbon Nanotubes for Highly Stable Room Temperature and Humidity-Tolerant Triethylamine Sensing

Triethylamine (TEA) poses a significant threat to our health and is extremely difficult to detect at the parts-per-billion (ppb) level at room temperature. Carbon nanotubes (CNTs) are versatile materials used in chemiresistive vapor sensing. However, achieving high sensitivity and selectivity with a low detection limit remains a challenge for pristine CNTs, hindering their widespread commercial application. To address these issues, we propose functionalized multiwalled CNTs (MWCNTs) with carboxylic acid (COOH)-based sensing channels for ultrasensitive TEA detection under ambient conditions. Advanced structural analyses confirmed the necessary modification of MWCNTs after functionalization. The sensor exhibited excellent sensitivity to TEA in air, with a superior noise-free signal (10 ppb), an extremely low limit of detection (LOD ≈ 0.8 ppb), excellent repeatability, and long-term stability under ambient conditions. Moreover, the response values became more stable, demonstrating excellent humidity resistance (40-80% RH). Notably, the functionalized MWCNT sensor exhibited improved response and recovery kinetics (200 and 400 s) to 10 ppm of TEA compared to the pristine MWCNT sensor (400 and 1300 s), and the selectivity coefficient for TEA gas was improved by approximately three times against various interferants, including ammonia, formaldehyde, nitrogen dioxide, and carbon monoxide. The remarkable improvements in TEA detection were mainly associated with the large specific surface area, abundant active sites, adsorbed oxygen, and other defects. The sensing mechanism was thoroughly explained by using Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and gas chromatography-mass spectrometry (GC-MS). This study provides a new platform for CNT-based chemiresistive sensors with high selectivity, low detection limits, and enhanced precision with universal potential for applications in food safety and environmental monitoring.