AIMC Topic: Spectrometry, Fluorescence
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Predicting Serotonin Detection with DNA-Carbon Nanotube Sensors across Multiple Spectral Wavelengths.
Journal of chemical information and modeling
May 13, 2024
Owing to the value of DNA-wrapped single-walled carbon nanotube (SWNT)-based sensors for chemically specific imaging in biology, we explore machine learning (ML) predictions DNA-SWNT serotonin sensor responsivity as a function of DNA sequence based o...
Rice Origin Tracing Technology Based on Fluorescence Spectroscopy and Stoichiometry.
Sensors (Basel, Switzerland)
May 9, 2024
The origin of agricultural products is crucial to their quality and safety. This study explored the differences in chemical composition and structure of rice from different origins using fluorescence detection technology. These differences are mainly...
Spectral fusion-based machine learning classifiers for discriminating membrane breakage in multiple scenarios.
Water research
May 2, 2024
Membrane breakage can lead to filtration failure, which allows harmful substances to enter the effluent, posing potential hazards to human health and the environment. This study is an innovative combination of fluorescence and ultraviolet-visible (UV...
Paper-based fluorescence sensor array with functionalized carbon quantum dots for bacterial discrimination using a machine learning algorithm.
Analytical and bioanalytical chemistry
Apr 17, 2024
The rapid discrimination of bacteria is currently an emerging trend in the fields of food safety, medical detection, and environmental observation. Traditional methods often require lengthy culturing processes, specialized analytical equipment, and b...
Ratiometric fluorescence sensor based on deep learning for rapid and user-friendly detection of tetracycline antibiotics.
Food chemistry
Apr 15, 2024
The detection of tetracycline antibiotics (TCs) in food holds great significance in minimizing their absorption within the human body. Hence, this study aims to develop a rapid, convenient, real-time, and accurate detection method for detecting antib...
Machine learning-assisted chromium speciation using a single-well ratiometric fluorescent nanoprobe.
Chemosphere
Apr 11, 2024
Chromium is widely recognized as a significant pollutant discharged into the environment by various industrial activities. The toxicity of this element is dependent on its oxidation state, making speciation analysis crucial for monitoring the quality...
Machine learning modeling of fluorescence spectral data for prediction of trace organic contaminant removal during UV/HO treatment of wastewater.
Water research
Mar 18, 2024
Dynamic feedback of the removal performance of trace organic contaminants (TrOCs) is essential towards economical advanced oxidation processes (AOPs), whereas the corresponding quick-response feedback methods have long been desired. Herein, machine l...
Machine learning and advanced statistical analysis for fluorescence correlation spectroscopy.
Biophysical journal
Feb 21, 2024
Neural network informed photon filtering reduces fluorescence correlation spectroscopy artifacts.
Biophysical journal
Feb 20, 2024
Fluorescence correlation spectroscopy (FCS) techniques are well-established tools to investigate molecular dynamics in confocal and super-resolution microscopy. In practice, users often need to handle a variety of sample- or hardware-related artifact...
Multiple marine algae identification based on three-dimensional fluorescence spectroscopy and multi-label convolutional neural network.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Jan 22, 2024
Accurate identification of algal populations plays a pivotal role in monitoring seawater quality. Fluorescence-based techniques are effective tools for quickly identifying different algae. However, multiple coexisting algae and their similar photosyn...
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