Rapid Quantification of Trimethylamine

Li, Zheng; Li, Hao; LaGasse, Maria K.; Suslick, Kenneth S.

doi:10.1021/acs.analchem.6b01170

Cited by 55 publications

(55 citation statements)

References 51 publications

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“…Quantitative measurement of color changes upon exposure to volatiles by digitally imaging provides a highly multidimensional response unique to the volatiles’ interactions with the sensor elements. Colorimetric sensor arrays have proven applications to environmental monitoring, medical diagnosis, security screening, and food safety…”

Section: Figurementioning

confidence: 99%

Colorimetric Recognition of Aldehydes and Ketones

et al. 2017

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A colorimetric sensor array has been designed for the identification of and discrimination among aldehydes and ketones in vapor phase. Due to rapid chemical reactions between the solid-state sensor elements and gaseous analytes, distinct color difference patterns were produced and digitally imaged for chemometric analysis. The sensor array was developed from classical spot tests using aniline and phenylhydrazine dyes that enable molecular recognition of a wide variety of aliphatic or aromatic aldehydes and ketones, as demonstrated by hierarchical cluster, principal component, and support vector machine analyses. The aldehyde/ketone-specific sensors were further employed for differentiation among and identification of ten liquor samples (whiskies, brandy, vodka) and ethanol controls, showing its potential applications in the beverage industry.

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Section: Figurementioning

confidence: 99%

Colorimetric Recognition of Aldehydes and Ketones

et al. 2017

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“…According to data dimensionality, sensor data are generally classified into three categories: 1) discrete, 2) time‐series, and 3) image data. 1)Discrete data: For applications such as food security, environmental monitoring, toxicity detection, pressure detection, and strain detection, one or several discrete data are often enough to evaluate the current status of a target object. Typical threshold evaluation, Euclidean distance, principal component analysis (PCA), and clustering methods are usually used to analyze these discrete sensor data . For example, in discriminating a specific class of chemical analytes, PCA and agglomerative hierarchical clustering approach have been proposed to process these sensor data collected from a colorimetric sensor array .…”

Section: Machine Learning and Edging Computingmentioning

confidence: 99%

“…Typical threshold evaluation, Euclidean distance, principal component analysis (PCA), and clustering methods are usually used to analyze these discrete sensor data. [340][341][342][343][344][345][346] For example, in discriminating a specific class of chemical analytes, PCA and agglomerative hierarchical clustering approach have been proposed to process these sensor data collected from a colorimetric sensor array. [341] The results of PCA (Figure 12a) and clustering statistical analysis indicate that the colorimetric method shows high discrimination ability and reproducibility, even down to amine concentrations of 50 ppm.…”

Section: Machine Learning and Edging Computingmentioning

confidence: 99%

Cyber–Physiochemical Interfaces

et al. 2020

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Living things rely on various physical, chemical, and biological interfaces, e.g., somatosensation, olfactory/gustatory perception, and nervous system response. They help organisms to perceive the world, adapt to their surroundings, and maintain internal and external balance. Interfacial information exchanges are complicated but efficient, delicate but precise, and multimodal but unisonous, which has driven researchers to study the science of such interfaces and develop techniques with potential applications in health monitoring, smart robotics, future wearable devices, and cyber physical/human systems. To understand better the issues in these interfaces, a cyber–physiochemical interface (CPI) that is capable of extracting biophysical and biochemical signals, and closely relating them to electronic, communication, and computing technology, to provide the core for aforementioned applications, is proposed. The scientific and technical progress in CPI is summarized, and the challenges to and strategies for building stable interfaces, including materials, sensor development, system integration, and data processing techniques are discussed. It is hoped that this will result in an unprecedented multi‐disciplinary network of scientific collaboration in CPI to explore much uncharted territory for progress, providing technical inspiration—to the development of the next‐generation personal healthcare technology, smart sports‐technology, adaptive prosthetics and augmentation of human capability, etc.

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“…The colorimetric sensor was deposited on nitrocellulose paper (Whatman, Maidstone, UK) with a robotic pin printer, as described in details in previous literature [31][32][33], which delivered nanoporous inks with silica-dye microspheres encapsulating a ketone-responsive indicator, pararosaniline. After printing, the sensor was dried under vacuum for 1 h at room temperature and stored in an N 2 atmosphere for at least 24 h before any measurements were taken.…”

Section: Preparation Of the Paper-based Sensormentioning

confidence: 99%

Nanoporous Silica-Dye Microspheres for Enhanced Colorimetric Detection of Cyclohexanone

2018

Chemosensors

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Forensic detection of non-volatile nitro explosives poses a difficult analytical challenge. A colorimetric sensor comprising of ultrasonically prepared silica-dye microspheres was developed for the sensitive gas detection of cyclohexanone, a volatile marker of explosives 1,3,5-trinitro-1,3,5-triazinane (RDX) and 1,3,5,7-tetranitro-1,3,5,7-tetrazocane (HMX). The silica-dye composites were synthesized from the hydrolysis of ultrasonically sprayed organosiloxanes under mild heating conditions (150 • C), which yielded microspherical, nanoporous structures with high surface area (~300 m 2 /g) for gas exposure. The sensor inks were deposited on cellulose paper and given sensitive colorimetric responses to trace the amount of cyclohexanone vapors even at sub-ppm levels, with a detection limit down to~150 ppb. The sensor showed high chemical specificity towards cyclohexanone against humidity and other classes of common solvents, including ethanol, acetonitrile, ether, ethyl acetate, and ammonia. Paper-based colorimetric sensors with hierarchical nanostructures could represent an alternative sensing material for practical applications in the detection of explosives.

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Rapid Quantification of Trimethylamine

Cited by 55 publications

References 51 publications

Colorimetric Recognition of Aldehydes and Ketones

Colorimetric Recognition of Aldehydes and Ketones

Cyber–Physiochemical Interfaces

Nanoporous Silica-Dye Microspheres for Enhanced Colorimetric Detection of Cyclohexanone

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