Machine learning
Research investigators have made a significant advancement in the field of machine learning by developing a fractal Langmuir adsorption model that accurately characterizes CH4 and CO2 adsorption in real shale. This breakthrough has far-reaching implications for CH4 enhanced recovery and CO2 geological sequestration. The study's findings were published
Machine learning
A new research study published in the journal Applied Energy has outlined the immense potential of machine learning (ML) in the field of chemical and environmental engineering. By leveraging the powerful data processing and pattern recognition capabilities of ML, researchers at the University of Science and Technology Beijing have successfully
Machine learning
Research has been conducted on upcycling polymeric waste into high-performance adsorbents for wastewater remediation. By fabricating a nanofibrous adsorbent using recovered poly(epsilon-caprolactone) (PCL) waste, a sustainable strategy was presented for wastewater treatment. This study aimed to enhance adsorption performance by incorporating Ti3C2Tx MXene and a microwave-assisted ionic liquid-functionalized composite.
Machine learning
Researchers from Xiangtan University have made a groundbreaking discovery in the field of post-combustion carbon capture, utilizing machine learning to enhance the energy efficiency of CO2 desorption. By developing a series of solid acid catalysts, the team was able to achieve a significant increase in CO2 desorption rate and cyclic
Machine learning
A new patent application has been filed for an image processing apparatus and method by inventors KOIKE, Tatsuki and TAKEMOTO, Kazuma. The application, filed on February 10, 2025, seeks to improve the accuracy of organ recognition for two-dimensional medical images. The inventors propose a system that trains a machine learning
Machine learning
A recent study at Kongju National University has made significant breakthroughs in accurately recording the colors of cultural heritage objects using digital photography and machine learning algorithms. The research aimed to reproduce colors that closely match those of the object, enabling precise documentation and analysis for conservation, restoration, and research
Machine learning
A team of researchers from the University of Bejaia, Algeria, has developed a new algorithm for detecting skin cancerous ulcers using image processing and machine learning techniques. The proposed method, dubbed DullRazor, aims to improve diagnostic accuracy by enhancing image quality and extracting key features from ulcer images. According to
Machine learning
Researchers from Brandenburg Technical University Cottbus Senftenberg have applied machine learning (ML) to optimize catalysts and process conditions for the efficient production of clean, renewable fuels through Fischer-Tropsch synthesis (FTS). The study, published in Chemie Ingenieur Technik, found that ML-based frameworks can model and optimize metal/zeolite catalysts for liquid
Machine learning
Researchers from the Chinese Research Academy of Environment Sciences have developed a data-driven strategy to design optimal catalysts for treating refractory organic wastewater. By integrating principal component analysis with correlation analysis, the team was able to link wastewater properties to catalyst structural descriptors, revealing stark differences in total organic carbon
Machine learning
Patent applicants Burns, Tanner; Cappel, Kwesi; Yeung, Kenneth have developed a system for the selective redaction of personally identifiable information (PII) in generative artificial intelligence (GenAI) model outputs. This innovation aims to prevent unauthorized access to sensitive data and ensure the security of enterprises' machine learning assets. The system
Machine learning
Researchers at the University of Florence have conducted a comprehensive review of methodologies for detecting rock mass fractures using 2D close-range photographs. The study emphasizes the importance of accurate fracture detection in geological assessments, stability in geoscience, geoengineering, and mining. The researchers employed machine learning and image processing approaches to
Machine learning
Research at Sichuan University in China has made significant progress in designing cost-effective and high-performance multifunctional electrocatalysts for the oxygen reduction reaction (ORR). The study explores the use of Ti2NO2 MXene and its derivatives as supports for single-atom catalysts (SACs), and their ORR electrocatalytic performances are investigated using density functional
