Detection of n-Propanol Down to the Sub-ppm Level Using p-Type Delafossite AgCrO2 Nanoparticles

Nose-on-Chip Nanobiosensors for Early Detection of Lung Cancer Breath Biomarkers

Lung cancer remains a global health concern, demanding the development of noninvasive, prompt, selective, and point-of-care diagnostic tools. Correspondingly, breath analysis using nanobiosensors has emerged as a promising noninvasive nose-on-chip technique for the early detection of lung cancer through monitoring diversified biomarkers such as volatile organic compounds/gases in exhaled breath. This comprehensive review summarizes the state-of-the-art breath-based lung cancer diagnosis employing chemiresistive-module nanobiosensors supported by theoretical findings. It unveils the fundamental mechanisms and biological basis of breath biomarker generation associated with lung cancer, technological advancements, and clinical implementation of nanobiosensor-based breath analysis. It explores the merits, challenges, and potential alternate solutions in implementing these nanobiosensors in clinical settings, including standardization, biocompatibility/toxicity analysis, green and sustainable technologies, life-cycle assessment, and scheming regulatory modalities. It highlights nanobiosensors' role in facilitating precise, real-time, and on-site detection of lung cancer through breath analysis, leading to improved patient outcomes, enhanced clinical management, and remote personalized monitoring. Additionally, integrating these biosensors with artificial intelligence, machine learning, Internet-of-things, bioinformatics, and omics technologies is discussed, providing insights into the prospects of intelligent nose-on-chip lung cancer sniffing nanobiosensors. Overall, this review consolidates knowledge on breathomic biosensor-based lung cancer screening, shedding light on its significance and potential applications in advancing state-of-the-art medical diagnostics to reduce the burden on hospitals and save human lives.

Chemiresistive effect of p-type delafossite CuScO2 microsheets to gaseous alcohols

The chemiresistive effect of an oxide significantly influences its electrical properties, which depend greatly on the interactions between the ambient gas molecules and the solid surface, including the gas adsorption and charge transfer still challenging to be clarified. In this work, we investigate the chemiresistive effect of the p-type delafossite CuScO2 microsheets by comparing their responses to various gaseous alcohols, which increase with an approximately linear relationship with the length of straight carbon chains from methanol to n-hexanol. A new mechanism is proposed to elucidate such a dramatic trend of observed chemiresistive change based on the first-principles calculations and test results. The increasing carbon chain length modulates the adsorption configuration and provides supplementary routes for electron transfer, which is assumed to account for the observed chemiresistive effect. This work may provide a novel perspective for the investigation and development of more advanced functional oxides for electrical applications.

Simultaneously Engineering Oxygen Defects and Heterojunction into Ho-Doped ZnO Nanoflowers for Enhancing n-Propanol Gas Detection

Lung cancer poses a serious threat to people's lives and health due to its high incidence rate and high mortality rate, making it necessary to effectively conduct early screening. As an important biomarker for lung cancer, the detection of n-propanol gas suffers from a low response value and a high detection limit. In this paper, flower-like Ho-doped ZnO was fabricated by the coprecipitation method for n-propanol detection at subppm concentrations. The gas sensor based on the 3% Ho-doped ZnO showed selectivity to n-propanol gas. Its response value to 100 ppm n-propanol was 341 at 140 °C, and its limit of detection (LOD) was about 25.6 ppb, which is lower than that of n-propanol in the breath of a healthy person (150 ppb). The calculation results show that the adsorption of n-propanol on a Ho-doped ZnO surface releases more energy than isopropanol, ethanol, formaldehyde, acetone, and ammonia. The enhanced gas-sensing properties of the Ho-doped ZnO material can be attributed to the fact that the Ho-doping distorts the crystal lattice of the ZnO, increases the specific surface area, and generates a large amount of oxygen defects. In addition, the doped Ho partially forms a Ho2O3/ZnO heterojunction in the material and improves the gas-sensing properties. The 3% Ho-doped ZnO material is expected to be a promising candidate for the trace detection of n-propanol gas.

Determination and Quantification of Acetaldehyde, Acetone, and Methanol in Hand Sanitizers Using Headspace GC/MS: Effect of Storage Time and Temperature

Accurate determination of the concentration of alcohols and their metabolites is important in forensics and in several life science areas. A new headspace gas chromatography-mass spectrometry method has been developed to quantify alcohols and their oxidative products using isotope-labeled internal standards. The limit of detection (LOD) of the analytes in the developed method was 0.211 µg/mL for methanol, 0.158 µg/mL for ethanol, 0.157 µg/mL for isopropanol, 0.010 µg/mL for n-propanol, 0.157 µg/mL for acetone, and 0.209 µg/mL for acetaldehyde. The precision and accuracy of the method were evaluated, and the relative standard deviation percentages were found to be less than 3%. This work demonstrates the application of this method, specifically in quantifying the concentration of oxidative products of alcohol and other minor alcohols found in hand sanitizers, which have become an essential household item since the COVID-19 pandemic. Apart from the major components, the minor alcohols found in hand sanitizers include methanol, isopropanol, and n-propanol. The concentration range of these minor alcohols found in ethanol-based hand sanitizer samples was as follows: methanol, 0.000921-0.0151 mg/mL; isopropanol, 0.454-13.8 mg/mL; and n-propanol, 0.00474-0.152 mg/mL. In ethanol-based hand sanitizers, a significant amount of acetaldehyde (0.00623-0.231 mg/mL) was observed as an oxidation product, while in the isopropanol-based hand sanitizer, acetone (0.697 mg/mL) was observed as an oxidation product. The concentration of acetaldehyde in ethanol-based hand sanitizers significantly increased with storage time and temperature, whereas no such increase in acetone concentration was observed in isopropanol-based hand sanitizers with storage time and temperature. In two of the selected hand sanitizers, the acetaldehyde levels increased by almost 200% within a week when stored at room temperature. Additionally, exposing the hand sanitizers to a temperature of 45 °C for 24 h resulted in a 100% increase in acetaldehyde concentration. On the contrary, the acetone level remained constant upon the change in storage time and temperature.

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.