For detection of gases and vapors in complex backgrounds, "classic" analytical instruments are an unavoidable alternative to existing sensors. Recently a new generation of sensors, known as multivariable sensors, emerged with a fundamentally different perspective for sensing to eliminate limitations of existing sensors. In multivariable sensors, a sensing material is designed to have diverse responses to different gases and vapors and is coupled to a multivariable transducer that provides independent outputs to recognize these diverse responses. Data analytics tools provide rejection of interferences and multi-analyte quantitation. This review critically analyses advances of multivariable sensors based on ligand-functionalized metal nanoparticles also known as monolayer-protected nanoparticles (MPNs). These MPN sensing materials distinctively stand out from other sensing materials for multivariable sensors due to their diversity of gas- and vapor-response mechanisms as provided by organic and biological ligands, applicability of these sensing materials for broad classes of gas-phase compounds such as condensable vapors and non-condensable gases, and for several principles of signal transduction in multivariable sensors that result in non-resonant and resonant electrical sensors as well as material- and structure-based photonic sensors. Such features should allow MPN multivariable sensors to be an attractive high value addition to existing analytical instrumentation.
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http://dx.doi.org/10.1039/c7cs00007c | DOI Listing |
Biomed Rep
February 2025
Faculty of Medicine and Life Sciences, University of Latvia, Riga LV-1004, Latvia.
Continuous glucose monitoring (CGM) has emerged as a superior method to glycated hemoglobin (HbA1c) monitoring for glycemic control assessment in type 1 diabetes (T1D). The association between CGM parameters and diabetic kidney disease (DKD) has not been extensively researched. The aim of the present study was to compare CGM metrics between patients with stable and progressive DKD and T1D.
View Article and Find Full Text PDFAnal Bioanal Chem
December 2024
Department of Analytical Chemistry and Organic Chemistry, Universitat Rovira i Virgili, Carrer Marcel·lí Domingo 1, 43007, Tarragona, Spain.
Analysing samples in their original form is increasingly crucial in analytical chemistry due to the need for efficient and sustainable practices. Analytical chemists face the dual challenge of achieving accuracy while detecting minute analyte quantities in complex matrices, often requiring sample pretreatment. This necessitates the use of advanced techniques with low detection limits, but the emphasis on sensitivity can conflict with efforts to simplify procedures and reduce solvent use.
View Article and Find Full Text PDFCell
December 2024
Program in Computational Biology and Biomedical Informatics, Yale University, New Haven, CT 06511, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06511, USA; Department of Computer Science, Yale University, New Haven, CT 06511, USA; Department of Biomedical Informatics and Data Science, Yale University, New Haven, CT 06511, USA; Department of Statistics and Data Science, Yale University, New Haven, CT 06511, USA. Electronic address:
Psychiatric disorders are influenced by genetic and environmental factors. However, their study is hindered by limitations on precisely characterizing human behavior. New technologies such as wearable sensors show promise in surmounting these limitations in that they measure heterogeneous behavior in a quantitative and unbiased fashion.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China.
Weather prediction is of great significance for human daily production activities, global extreme climate prediction, and environmental protection of the Earth. However, the existing data-based weather prediction methods cannot adequately capture the spatial and temporal evolution characteristics of the target region, which makes it difficult for the existing methods to meet practical application requirements in terms of efficiency and accuracy. Changes in weather involve both strongly correlated spatial and temporal continuation relationships, and at the same time, the variables interact with each other, so capturing the dynamic correlations among space, time, and variables is particularly important for accurate weather prediction.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy.
This study investigates the effectiveness of amplitude transformation in enhancing the performance and robustness of Multiscale Fuzzy Entropy for Alzheimer's disease detection using electroencephalography signals. Multiscale Fuzzy Entropy is a complexity measure particularly sensitive to intra- and inter-subject variations in signal amplitude, as well as the selection of key parameters such as embedding dimension () and similarity criterion (), which often result in inconsistent outcomes when applied to multivariate data, such as electroencephalography signals. To address these challenges and to generalize the possibility of adopting Multiscale Fuzzy Entropy as a diagnostic tool for Alzheimer's disease, this research explores amplitude transformation preprocessing on electroencephalography signals in Multiscale Fuzzy Entropy calculation across varying parameters.
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