Molecular in situ monitoring of the pH-triggered response in adaptive polymers by two-dimensional Raman micro-correlation-spectroscopy

Ag(I) Ion-Concentration-Dependent Dynamic Mechanism of Thiolactic-Acid-Capped Gold Nanoclusters Revealed by Fluorescence Spectra and Two-Dimensional Correlation Spectroscopy

Based on fluorescence spectroscopy, being combined with several spectral analysis techniques including principal component analysis (PCA), two-dimensional correlation spectroscopy (2D-COS), and moving window 2D-COS, the study disclosed the structural variations of gold nanoclusters capped by thiolactic acid (AuNCs@TLA) induced by Ag(I) ions. Transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS) were applied to monitor the morphology evolution of the surface and composition of the nanoclusters induced by Ag(I) ions. Several spectral components, centered at (790, 607) nm, (670, 590) nm, and (740, 670) nm were revealed by 2D-COS analysis, suggesting new luminescent species or groups were generated with the introduction of Ag(I) ions. A two-stage mechanism was revealed for the photoluminescence variations of AuNCs@TLA induced by Ag(I) ion. The first stage was characterized by the emission quench of 790 nm followed by the emerging emission of 607 nm, which was attributed to the anti-galvanic reaction; and the second stage featured by the noticeable growth of the emission's intensity around 670 nm as result of the AuNCs' size effect. The present study will attract more focuses on near-infrared (NIR)-emitted metal nanoclusters and promote their synthesis and utilities.

Enhanced 3-D asynchronous correlation data preprocessing method for Raman spectroscopy of Chinese handmade paper

We have developed a novel 3D asynchronous correlation method (3D-ACM) designed for the classification and identification of Chinese handmade paper samples using Raman spectra and machine learning. The 3D-ACM approach involves two rounds of tensor product and Hilbert transform operations. In the tensor product process, the outer product of the spectral data from different samples within the same category is computed, establishing inner connections among all samples within that category. The Hilbert transform introduces a 90-degree phase shift, resulting in a true three-dimensional spectral data structure. This expansion significantly increases the number of equivalent frequency points and samples within each category. This enhancement substantially boosts spectral resolution and reveals more hidden information within the spectral data. To maximize the potential of 3D-ACM, we employed six machine learning models: principal component analysis (PCA) with linear regression (LR), support vector machine (SVM) with LR, k-Nearest Neighbors (KNN), random forest (RF), and convolutional neural network (CNN). When applied to the 3D-ACM data preprocessing method, R-squared values of PLS-LR, KNN, RF and CNN supervised models, approached or equaled 1. This indicates exceptional performance comparable to unsupervised models like PCA. 3D-ACM stands as a versatile mathematical technique not confined to spectral data. It also eliminates the necessity for additional experimental setups or external control conditions, distinct from traditional two-dimensional correlation spectroscopy. Moreover, it preserves the original experimental data, setting it apart from conventional data preprocessing methods. This positions 3D-ACM as a promising tool for future material classification and identification in conjunction with machine learning.