Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
In this study, we developed prediction models for the adsorption of divalent and trivalent oxyanions on goethite based on machine learning algorithms. After verifying the reliability of the models, the importance of goethite specific surface area (SSA) and the average oxyanion adsorption capacities of goethite with different SSAs were calculated by shapley additive explanations (SHAP) importance analysis and partial dependence (PD) analysis. Despite there were differences in the feature importance of divalent and trivalent oxyanions, the contribution of goethite's SSA to the adsorption amount ranked the fourth based on SHAP importance, indicating SSA played the important role in oxyanion adsorption. Meanwhile, the PD values of SSA and the optimized complexation constants from surface complexation modeling (SCM) both indicated a non-monotonic relationship between the goethite with different SSA and its oxyanions binding capacity. When the total site concentration and crystal face composition were used as the machine learning model input features, the SHAP importance values of crystal faces and the PD decomposition results indicated that the (001) face showed the crucial influence on oxyanions adsorption amount. These findings demonstrated the important role of crystal face composition in goethite's adsorption ability, and provided a theoretical explanation for the variations of oxyanions adsorption amount on different SSA goethite.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.envpol.2023.123162 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Driven by eutrophication and global warming, the occurrence and frequency of harmful cyanobacteria blooms (CyanoHABs) are increasing worldwide, posing a serious threat to human health and biodiversity. Early warning enables precautional control measures of CyanoHABs within water bodies and in water works, and it becomes operational with high frequency in situ data (HFISD) of water quality and forecasting models by machine learning (ML). However, the acceptance of early warning systems by end-users relies significantly on the interpretability and generalizability of underlying models, and their operability.
View Article and Find Full Text PDFPredicting left-without-being-seen in an emergency department as a dynamic risk.
Am J Emerg Med
September 2025
University of Toronto, Rotman School of Management, Canada.
Study Objective: Accurately predicting which Emergency Department (ED) patients are at high risk of leaving without being seen (LWBS) could enable targeted interventions aimed at reducing LWBS rates. Machine Learning (ML) models that dynamically update these risk predictions as patients experience more time waiting were developed and validated, in order to improve the prediction accuracy and correctly identify more patients who LWBS.
Methods: The study was deemed quality improvement by the institutional review board, and collected all patient visits to the ED of a large academic medical campus over 24 months.
Predicting In-Hospital Cardiac Arrest Using Machine Learning Models: Protocol for a Scoping Review.
JMIR Res Protoc
September 2025
University of Nevada, Las Vegas, Las Vegas, NV, United States.
Background: In-hospital cardiac arrest (IHCA) remains a public health conundrum with high morbidity and mortality rates. While early identification of high-risk patients could enable preventive interventions and improve survival, evidence on the effectiveness of current prediction methods remains inconclusive. Limited research exists on patients' prearrest pathophysiological status and predictive and prognostic factors of IHCA, highlighting the need for a comprehensive synthesis of predictive methodologies.
View Article and Find Full Text PDFDual-Motor Oxygen-Deficiency-Engineering Al-Doped WO Circular Nanorod Arrays for Parts per Billion-Level Acetone Detection.
Nano Lett
September 2025
School of Materials and Chemistry, University of Shanghai for Science & Technology, Shanghai 200093, China.
Developing low-temperature gas sensors for parts per billion-level acetone detection in breath analysis remains challenging for non-invasive diabetes monitoring. We implement dual-defect engineering via one-pot synthesis of Al-doped WO nanorod arrays, establishing a W-O-Al catalytic mechanism. Al doping induces lattice strain to boost oxygen vacancy density by 31.
View Article and Find Full Text PDFIdentification and Exploration of Novel B Cell Infiltration-Related Biomarkers in Endometriosis.
Am J Reprod Immunol
September 2025
Department of Laboratory Animal Science, Kunming Medical University, Kunming, China.
Objective: To explore B cell infiltration-related genes in endometriosis (EM) and investigate their potential as diagnostic biomarkers.
Methods: Gene expression data from the GSE51981 dataset, containing 77 endometriosis and 34 control samples, were analyzed to detect differentially expressed genes (DEGs). The xCell algorithm was applied to estimate the infiltration levels of 64 immune and stromal cell types, focusing on B cells and naive B cells.