A graphene coated microfiber Bragg grating (GMFBG) for gas sensing is reported in this Letter. Taking advantage of the surface field enhancement and gas absorption of a GMFBG, we demonstrate an ultrasensitive approach to detect the concentration of chemical gas. The obtained sensitivities are 0.2 and 0.5 ppm for NH3 and xylene gas, respectively, which are tens of times higher than that of a GMFBG without graphene for tiny gas concentration change detection. Experimental results indicate that the GMFBG-based NH3 gas sensor has fast response due to its highly compact structure. Such a miniature fiber-optic element may find applications in high sensitivity gas sensing and trace analysis.
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http://dx.doi.org/10.1364/OL.39.001235 | DOI Listing |
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December 2024
Centre for Nanoscience and Engineering, Indian Institute of Science, Bengaluru, 560012, India.
The design of mixed-dimensional heterostructures has emerged to be a new frontier of research as it induces exciting physical/chemical properties that extend beyond the fundamental properties of single dimensional systems. Therefore, rational design of heterostructured materials with novel surface chemistry and tailored interfacial properties appears to be very promising for the devices such as the gas sensors. Here, a highly sensitive gas sensor device is constructed by employing heterostructures of boron doped molybdenum disulfide quantum dots (B-MoS Qdots) assembled into the matrix of TiCT MXene.
View Article and Find Full Text PDFPhotoacoustics
February 2025
College of Control Science & Engineering, China University of Petroleum (East China), Qingdao 266580, PR China.
Traditional beat frequency quartz-enhanced photoacoustic spectroscopy (BF-QEPAS) are limited by short energy accumulation times and the necessity of a decay period, leading to weaker signals and longer measurement cycles. Herein, we present a novel optomechanical energy-enhanced (OEE-) BF-QEPAS technique for fast and sensitive gas sensing. Our approach employs periodic pulse-width modulation (PWM) of the laser signal with an optimized duty cycle, maintaining the quartz tuning fork's (QTF) output at a stable steady-state level by applying stimulus signals at each half-period and allowing free vibration in alternate half-periods to minimize energy dissipation.
View Article and Find Full Text PDFTalanta
December 2024
Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Military Medical Sciences Academy, Tianjin, 300050, China. Electronic address:
The detection of ammonia (NH)gas holds significant importance in both daily life and industrial production. In this study, the NbCT/MoSe sensor was synthesized using a one-step hydrothermal method and applied for NH detection. The morphology and elemental composition of the composites were analyzed through a series of characterization techniques including XRD, TEM, SEM, and XPS, confirming the successful synthesis of NbCT/MoSe composite with the optimal mass ratio.
View Article and Find Full Text PDFACS Cent Sci
December 2024
Department of Chemistry, Shanghai Stomatological Hospital & School of Stomatology, State Key Laboratory of Molecular Engineering of Polymers, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Fudan University, Shanghai 200433, P. R. China.
An efficient regiospecific co-assembly (RSCA) strategy is developed for general synthesis of mesoporous metal oxides with pore walls precisely decorated by highly dispersed noble metal nanocrystals with customized parameters (diameter and composition). It features the rational utilization of the specific interactions between hydrophilic molecular precursors, hydrophobic noble metal nanocrystals, and amphiphilic block copolymers, to achieve regiospecific co-assembly as confirmed by molecular dynamics simulations. Through this RSCA strategy, we achieved a controllable synthesis of a variety of functional mesoporous metal oxide composites (e.
View Article and Find Full Text PDFFront Big Data
December 2024
Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, United States.
Introduction: Self-efficacy is a critical determinant of students' academic success and overall life outcomes. Despite its recognized importance, research on predictors of self-efficacy using machine learning models remains limited, particularly within Muslim societies. This study addresses this gap by leveraging advanced machine learning techniques to analyze key factors influencing students' self-efficacy.
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