In this research, fluorogenic labelling followed by applying first-order derivative spectrofluorimetry for the developed fluorophore is discussed as an alternative, sensitive and selective analytical approach. Benoxinate, containing a primary amine and fluorescamine reagent were selected for the study. Then the proposed methodology relies on the reaction between the primary amine in benoxinate with fluorescamine that selectively reacts with primary amines to develop highly fluorescent products. The fluorescamine-benoxinate developed fluorophore is identified by its sharp first-order derivative peak at 465 nm following excitation at 386 nm in borate buffer, pH 8. The optimum reaction conditions were ascertained. Following ICH validation guidelines, the first order derivative of the relative fluorescence intensity for the developed fluorophore was linearly related to benoxinate concentration and ranged from 20.0 to 200.0 ng/mL with a detection limit of 3.36 ng/mL and a quantitation limit of 10.19 ng/mL, moreover, satisfying accuracy and precision values were obtained upon statistical analysis of results. The offered analytical method was successfully applied to quantify benoxinate in raw material and Benox® eye drops as a direct application to a commercial formulation.
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http://dx.doi.org/10.1016/j.saa.2023.123410 | DOI Listing |
J Environ Manage
January 2025
School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran.
Municipal solid waste (MSW) landfills significantly contribute to global methane gas production, underscoring the critical need for accurate emission gas estimation within an effective gas management strategy. While first-order models such as LandGEM are essential for estimating gas emissions, their lack of accuracy has spurred numerous studies to enhance core parameters, specifically methane generation rate constant (k) and potential methane generation capacity (L). In this study, various machine learning models were used to generate modified LandGEM model parameters to reduce the error of methane gas estimations by the model.
View Article and Find Full Text PDFEnviron Technol
January 2025
Department of Materials Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
This study synthesises expanded graphite (EG) from graphitised carbon from waste polyethylene terephthalate (PET) bottles. The adsorbent material was characterised using FTIR, XRF, XRD, SEM, Raman Spectroscopy, and BET surface area analysis. The synthesised EG defluorinated wastewater, utilising response surface methodology (RSM) for experimental design and optimisation.
View Article and Find Full Text PDFSci Rep
December 2024
School of Electrical Engineering, Vellore Institute of Technology, Chennai, 600127, India.
Spherical tanks have been predominantly used in process industries due to their large storage capability. The fundamental challenges in process industries require a very efficient controller to control the various process parameters owing to their nonlinear behavior. The current research work in this paper aims to propose the Approximate Generalized Time Moments (AGTM) optimization technique for designing Fractional-Order PI (FOPI) and Fractional-Order PID (FOPID) controllers for the nonlinear Single Spherical Tank Liquid Level System (SSTLLS).
View Article and Find Full Text PDFFront Microbiol
December 2024
Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Shenyang, China.
Aim: The current study aims to delineate subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), the sacrospinalis muscle, and all abdominal musculature at the L3-L5 vertebral level from non-contrast computed tomography (CT) imagery using deep learning algorithms. Subsequently, radiomic features are collected from these segmented images and subjected to medical interpretation.
Materials And Methods: This retrospective analysis includes a cohort of 315 patients diagnosed with acute necrotizing pancreatitis (ANP) who had undergone comprehensive whole-abdomen CT scans.
Med Decis Making
December 2024
Department of Health Policy, Stanford School of Medicine, Stanford, CA, USA.
Purpose: Individual-level state-transition microsimulations (iSTMs) have proliferated for economic evaluations in place of cohort state transition models (cSTMs). Probabilistic economic evaluations quantify decision uncertainty and value of information (VOI). Previous studies show that iSTMs provide unbiased estimates of expected incremental net monetary benefits (EINMB), but statistical properties of iSTM-produced estimates of decision uncertainty and VOI remain uncharacterized.
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