Enhancing advanced oxidation processes: the role of AI in heterogeneous catalysis.

Sci Bull (Beijing)

Korea Biochar Research Center & Division of Environmental Science and Ecological Engineering, Korea University, Seoul, 02841, Republic of Korea. Electronic address:

Published: March 2025


Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.scib.2025.01.022DOI Listing

Publication Analysis

Top Keywords

enhancing advanced
4
advanced oxidation
4
oxidation processes
4
processes role
4
role heterogeneous
4
heterogeneous catalysis
4
enhancing
1
oxidation
1
processes
1
role
1

Similar Publications

Despite advancements in systemic therapy, the mortality rate for patients with metastatic melanoma remains around 70%, underscoring the imperative for alternative treatment strategies. Through the establishment of a chemoresistant melanoma model and a subsequent drug investigation, we have identified pacritinib, a medication designed for treating myelofibrosis and severe thrombocytopenia, as a potential candidate to overcome resistance to melanoma therapy. Our research reveals that pacritinib, administered at clinically achievable concentrations, effectively targets dacarbazine-resistant melanoma cells by suppressing IRAK1 rather than JAK2.

View Article and Find Full Text PDF

Treating neurological disorders is challenging due to the blood-brain barrier (BBB), which limits therapeutic agents, including proteins and peptides, from entering the central nervous system. Despite their potential, the BBB's selective permeability is a significant obstacle. This review explores recent advancements in protein therapeutics for BBB-targeted delivery and highlights computational tools.

View Article and Find Full Text PDF

Multimodal self-supervised retinal vessel segmentation.

Neural Netw

September 2025

Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China. Electronic address:

Automatic segmentation of retinal vessels from retinography images is crucial for timely clinical diagnosis. However, the high cost and specialized expertise required for annotating medical images often result in limited labeled datasets, which constrains the full potential of deep learning methods. Recent advances in self-supervised pretraining using unlabeled data have shown significant benefits for downstream tasks.

View Article and Find Full Text PDF

Microfluidic paper-based analytical devices for food spoilage detection: emerging trends and future directions.

Talanta

September 2025

Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Viet Nam. Electronic address:

Food spoilage poses a global challenge with far-reaching consequences for public health, economic stability, and environmental sustainability. Conventional analytical methods for spoilage detection though accurate are often cost-prohibitive, labor-intensive, and unsuitable for real-time or field-based monitoring. Microfluidic paper-based analytical devices (μPADs) have emerged as a transformative technology offering rapid, portable, and cost-effective solutions for food quality assessment.

View Article and Find Full Text PDF

Applications of Federated Large Language Model for Adverse Drug Reactions Prediction: Scoping Review.

J Med Internet Res

September 2025

Department of Information Systems and Cybersecurity, The University of Texas at San Antonio, 1 UTSA Circle, San Antonio, TX, 78249, United States, 1 (210) 458-6300.

Background: Adverse drug reactions (ADR) present significant challenges in health care, where early prevention is vital for effective treatment and patient safety. Traditional supervised learning methods struggle to address heterogeneous health care data due to their unstructured nature, regulatory constraints, and restricted access to sensitive personal identifiable information.

Objective: This review aims to explore the potential of federated learning (FL) combined with natural language processing and large language models (LLMs) to enhance ADR prediction.

View Article and Find Full Text PDF