Novel graphene-like nanomaterials with a non-zero bandgap are important for the design of gas sensors. The selectivity toward specific targets can be tuned by introducing appropriate functional groups on their surfaces. In this study, we use first-principles simulations, in the form of density functional theory (DFT), to investigate the covalent functionalization of a single-layer graphitized BCN with azides to yield aziridine-functionalized adducts and explore their possible use to realize ammonia sensors. First, we determine the most favorable sites for physical adsorption and chemical reaction of methylnitrene, arising from the decomposition of methylazide, onto a BCN monolayer. Then, we examine the thermodynamics of the [1 + 2]-cycloaddition reaction of various phenylnitrenes and perfluorinated phenylnitrenes para-substituted with (R = COH, SOH) groups, demonstrating favorable energetics. We also monitor the effect of the functionalization on the electronic properties of the nanosheets via density of states and band structure analyses. Finally, we test four dBCN to gBCN substrates in the sensing of ammonia. We show that, thanks to their hydrogen bonding capabilities, the functionalized BCN can selectively detect ammonia, with interaction energies varying from -0.54 eV to -1.37 eV, even in presence of competing gas such as COand HO, as also confirmed by analyzing the change in the electronic properties and the values of recovery times near ambient temperature. Importantly, we model the conductance of a selected substrate alone and in presence of NHto determine its effect on the integrated current, showing that humidity and coverage conditions should be properly tuned to use HOC-functionalized BCN-based nanomaterials to develop selective gas sensors for ammonia.
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http://dx.doi.org/10.1088/1361-6528/ad64da | DOI Listing |
Br J Anaesth
February 2025
Transfusion Research Unit, Department of Public Health and Preventative Medicine, Monash University, Melbourne, VIC, Australia; Department of Clinical Haematology, Monash Health, Clayton, VIC, Australia.
Accurate and timely diagnostic information is a vital adjunct to clinical assessment to inform therapeutic decision-making, including decisions to transfuse, or not transfuse, blood components. A prospective cohort study of diagnostic point-of-care (POC) haemoglobin measurements on arterial or central venous samples from adults undergoing major noncardiac surgery compared three widely used devices, HemoCue®, i-STAT™, and the Rad-67™ pulse CO-Oxymeter® finger sensor device, against standard laboratory haemoglobin measurements, but importantly not against a blood gas analyser. The study focused on haemoglobin results below 100 g L to establish the utility of these devices to guide red cell transfusion decisions.
View Article and Find Full Text PDFAnal Chem
January 2025
School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China.
As breath nitric oxide (NO) is a biomarker of respiratory inflammation, reliable techniques for the online detection of ppb-level NO in exhaled breath are essential for the noninvasive diagnosis of respiratory inflammation. Here, we report a breath NO sensor based on the multiperiodic spectral reconstruction neural network. First, a spectral reconstruction method that transforms a spectrum from the wavelength domain to the intensity domain is proposed to remove noise and interference signals from the spectrum.
View Article and Find Full Text PDFJ Hazard Mater
January 2025
BP Australia Pty Ltd, Melbourne, Victoria 3000, Australia.
Natural Source Zone Deletion (NSZD) is a viable long-term management option for sites impacted by petroleum hydrocarbon fuels. NSZD rate estimation methods for petroleum mass losses often use soil gas gradients of oxygen, carbon dioxide, methane or vapour concentrations through the vadose zone. Seeking greater efficiencies, we investigated if existing short-screened wells are reliable for representative sampling of soil gases in a vadose zone undergoing NSZD.
View Article and Find Full Text PDFACS Sustain Resour Manag
January 2025
Department of Applied Mathematics and Statistics, Colorado School of Mines, Golden, Colorado 80401, United States.
We propose a generic, modular framework to optimize the placement of point-in-space continuous monitoring system sensors on oil and gas sites aiming to maximize the methane emission detection efficiency. Our proposed framework substantially expands the problem scale compared to previous related studies and can be adapted for different objectives in sensor placement. This optimization framework is comprised of five steps: (1) simulate emission scenarios using site-specific wind and emission information; (2) set possible sensor locations under consideration of the site layout and any site-specific constraints; (3) simulate methane concentrations for each pair of emission scenario and possible sensor location; (4) determine emissions detection based on the site-specific simulated concentrations; and (5) select the best subset of sensor locations, under a given number of sensors to place, using genetic algorithms combined with Pareto optimization.
View Article and Find Full Text PDFInterest in carbon dioxide (CO) sensors is growing rapidly due to the increasing awareness of the link between air quality and health. Indoor, high CO levels signal poor ventilation, and outdoor the burning of fossil fuels and its associated pollution. CO gas sensors based on integrated optical waveguides are a promising solution due to their excellent gas sensing selectivity, compact size, and potential for mass manufacturing large volumes at low cost.
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