Identifying Chemical, Physical, and Instrumental Matrix Matched Samples by Leveraging Spectral Model Regression Vectors

Using multilinear regressions developed from excitation-emission matrices to estimate the wastewater content in urban streams impacted by sanitary sewer leaks and overflows

Failing sewer infrastructure introduces unknown quantities of raw wastewater into urban streams, raising human and ecological health concerns. To address this problem, we developed multilinear regressions that relate fluorescent dissolved organic matter to wastewater content. The models were constructed with the area-normalized regional volumes of excitation-emission matrices measured for mixtures of deionized water, surface water from a wastewater-impacted stream, wastewater from a sanitary sewer adjacent to the stream, and Suwannee River natural organic matter. The best performing multilinear regression had a standard error of 0.55 % wastewater. A matrix-matched calibration was used to internally validate the approach and confirm the wastewater content of select samples. The multilinear model was externally validated through (i) comparison to concentrations of contaminants of emerging concern in surface water and wastewater and (ii) extension to samples from previous campaigns that employed alternative wastewater indicators. Using the validated model, we estimated an average wastewater content of 2.4 ± 4.0 % in 165 samples collected from 14 locations in the Gwynns Falls watershed (USA) between April 2019 and April 2023. The maximum wastewater content was 35 % at a site where sanitary sewer leaks and overflows have been previously documented. The reported approach represents a cost-effective and scalable technique to estimate wastewater content in urban streams through analysis of fluorescent dissolved organic matter.

Physicochemical Responsive Integrated Similarity Measure (PRISM) for a Comprehensive Quantitative Perspective of Sample Similarity Dynamically Assessed with NIR Spectra

Determining sample similarity underlies many foundational principles in analytical chemistry. For example, calibration models are unsuitable to predict outliers. Calibration transfer methods assume a moderate degree of sample and measurement dissimilarities between a calibration set and target prediction samples. Classification approaches link target sample similarities to groups of similar class samples. Although similarity is ubiquitous in analytical chemistry and everyday life, quantifying sample similarity is without a straightforward solution, especially when target domain samples are unlabeled and the only known features are measurable, such as spectra (the focus of this paper). The process proposed to assess sample similarity integrates spectral similarity information with contextual considerations among source analyte contents, model, and analyte predictions. This hybrid approach named the physicochemical responsive integrated similarity measure (PRISM) amplifies hidden-but-essential physicochemical properties encoded within respective spectra. PRISM is tested on four near-infrared (NIR) data sets for four diverse application areas to show efficacy. These applications are the assessment of prediction reliability and model updating for model generalizability, outlier detection, and basic matrix matching evaluation. Discussion is provided on adapting PRISM to classification problems. Results indicate that PRISM collects large amounts of similarity information and effectively integrates it to produce a quantitative similarity evaluation between the target sample and a source domain. The approach is also useful for biological samples with additional physiochemical variations. While PRISM is dynamically tested on NIR data, parts of PRISM were previously applied to other data types, and PRISM should be applicable to other measurement systems perturbed by matrix effects.

Identification of host-guest systems in green TADF-based OLEDs with energy level matching based on a machine-learning study

Booming progress has been made in both the molecular design concept and the fundamental electroluminescence (EL) mechanism of thermally activated delayed fluorescence (TADF)-based organic light-emitting diodes (OLEDs) in recent years. One of the requirements for TADF-based OLEDs having high external quantum efficiency (EQE) is the favorable energy level alignment between the host and the guest to promote the energy transfer and improve the carrier balance. However, strategies to optimize the TADF-based OLED performance by selecting suitable host-guest systems in the light-emitting layer are far from enough. In this work, we investigated guest-host systems through the use of two machine-learning approaches (feature-based and similarity-based algorithms) from our recent effort for the optimization of TADF-based OLEDs. The Random Forest (RF) algorithm based on the features of electronic and photo-physical properties can accurately predict the EQE of green TADF-based OLEDs with average correlation coefficients of R2 = 0.85 for the training set and R2 = 0.74 for the testing set. Also, the Support Vector Regression (SVR) algorithm based on similarity metrics between pairs of materials (e.g., host and guest) in terms of electronic parameters can provide reasonable device performance prediction (R2 = 0.72) through the optimization procedure of the parameters. These results show that the predictive capability and model applicability of both machine-learning models can be used to identify suitable host-guest systems and explore complex relationships in green TADF-based OLEDs.