AIMC Topic: Spectrophotometry, Infrared

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Toward Complete Molecular Structure Prediction from Infrared Spectroscopy Using Deep Learning.

Journal of chemical information and modeling
Infrared (IR) spectroscopy is a broadly used tool to solve the molecular structures of unknown compounds. Though the theory of generating IR spectra from molecules is well established, the inverse problem of solving molecular structures from given sp...

A machine learning protocol for predicting structural distributions of amyloid-forming proteins from 2D IR spectra.

Proceedings of the National Academy of Sciences of the United States of America
Protein misfolding plays a central role in diseases such as Alzheimer's disease, Parkinson's disease, type 2 diabetes, and transthyretin amyloidosis (ATTR), often driven by specific aggregation-prone segments such as A and A of amyloid-42 (A42), -Syn...

Towards scalable age-grading of Aedes albopictus mosquito using mid-infrared spectroscopy and machine learning.

Scientific reports
The age structure and dynamics of mosquito populations are crucial for understanding their ability to spread diseases and assessing the effectiveness of anti-mosquito control measures. However, available methods to age-grade mosquito populations are ...

LUMIR: an LLM-driven unified agent framework for multi-task infrared spectroscopy reasoning.

Analytica chimica acta
Infrared spectroscopy enables rapid and non-destructive characterization of chemical and material properties, yet effective analysis typically requires workflows involving preprocessing, variable selection, and modeling. The construction and optimiza...

IR Spectroscopy: From Experimental Spectra to High-Resolution Structural Analysis by Integrating Simulations and Machine Learning.

The journal of physical chemistry. B
Understanding biomolecular function at the atomic scale requires detailed insight into the structural changes underlying dynamic processes. Vibrational infrared (IR) spectroscopy─when paired with biomolecular simulations and quantum-chemical calculat...

Etiology-Agnostic Diagnosis of Early Myocardial Ischemia via AI-Driven Label-Free Spectral Histopathology.

Analytical chemistry
Myocardial ischemia is a core pathological mechanism in diverse fatal diseases and can be triggered by multiple factors. Diagnosing early myocardial ischemia (EMI) caused by nontraditional factors (e.g., drugs or stress) remains challenging due to su...

Calibration for Quantitative Chemical Analysis in IR Microscopic Imaging.

Analytical chemistry
Infrared spectroscopy of macroscopic samples can be calibrated against reference analysis, such as lipid profiles acquired by gas chromatography, and serve as a fast, low-cost, quantitative analytical method. Calibration of infrared microspectroscopi...

Machine Learning-Aided Screening and Design Rule Discovery for LWIR-Transparent Optical Materials.

Journal of chemical information and modeling
The development of low-cost, high-performance materials with enhanced transparency in the long-wavelength infrared (LWIR) region (800-1250 cm/8-12.5 μm) is essential for advancing thermal imaging and sensing technologies. Traditional LWIR optics rely...

Detection of Unlabeled Polystyrene Micro- and Nanoplastics in Mammalian Tissue by Optical Photothermal Infrared Spectroscopy.

Analytical chemistry
In this study, we investigate the efficacy of optical photothermal infrared (O-PTIR) spectroscopy, also known as mid-infrared photothermal (MIP) microscopy, for label-free and nondestructive detection of micro- and nanoplastics (MNPs) down to diamete...

Optimizing super-feature selection for machine learning-enhanced spectroscopic analysis in biomedical research.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
PURPOSE: Machine-learning-powered label-free infrared spectroscopic methods offer significant potential for diagnostic and biomedical applications. However, their applications have been limited by spectral noise, where critical features are often obs...