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Carbon Dioxide

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Machine Learning Accelerated Discovery of Covalent Organic Frameworks for Environmental and Energy Applications.

Environmental science & technology
Covalent organic frameworks (COFs) are porous crystalline materials obtained by linking organic ligands covalently. Their high surface area and adjustable pore sizes make them ideal for a range of applications, including CO capture, CH storage, gas s...

Intelligent monitoring of fruit and vegetable freshness in supply chain based on 3D printing and lightweight deep convolutional neural networks (DCNN).

Food chemistry
In this study, an innovative intelligent system for supervising the quality of fresh produce was proposed, which combined 3D printing technology and deep convolutional neural networks (DCNN). Through 3D printing technology, sensitive, lightweight, an...

Enhancing process monitoring and control in novel carbon capture and utilization biotechnology through artificial intelligence modeling: An advanced approach toward sustainable and carbon-neutral wastewater treatment.

Chemosphere
Integrating carbon capture and utilization (CCU) technologies into wastewater treatment plants (WWTPs) is essential for mitigating greenhouse gas (GHG) emissions and enhancing environmental sustainability, but further advancements in process monitori...

Computational intelligence modeling and optimization of small molecule API solubility in supercritical solvent for production of drug nanoparticles.

Scientific reports
Artificial Intelligence (AI) is applied in this research for the analysis of a novel green method for production of nanomedicine. The method is based on supercritical solvent for production of drug nanoparticles in which the AI was used to estimate t...

A sustainable industrial waste control with AI for predicting CO2 for climate change monitoring.

Journal of environmental management
As the challenge of climate change continues to grow, we need creative solutions to predict better and track industrial waste carbon emissions, focusing on sustainable waste management practices. The present study proposes a state-of-the-art Metavers...

Predicting the solubility of drugs in supercritical carbon dioxide using machine learning and atomic contribution.

European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V
The pharmaceutical sector is aware of supercritical CO (SC-CO) as a possible replacement for problematic organic solvents. Using a novel artificial intelligence (AI) strategy to predict drug solubility using the SC-CO system mathematically has been d...

Improved Solubility Predictions in scCO Using Thermodynamics-Informed Machine Learning Models.

Journal of chemical information and modeling
Accurate solubility prediction in supercritical carbon dioxide (scCO) is crucial for optimizing experimental design by eliminating unnecessary and costly trials at an early stage, thereby streamlining the workflow. A comprehensive solubility database...

Optimization of CO absorption into MDEA-PZ-sulfolane hybrid solution using machine learning algorithms and RSM.

Environmental science and pollution research international
This study presents the modeling and simulation of carbon dioxide (CO₂) absorption in hybrid amine solutions using machine learning algorithms and response surface methodology (RSM). The process was governed by adjustable input parameters, including ...

Leveraging explainable AI to predict soil respiration sensitivity and its drivers for climate change mitigation.

Scientific reports
Global warming is one of the most pressing and critical problems facing the world today. It is mainly caused by the increase in greenhouse gases in the atmosphere, such as carbon dioxide (CO). Understanding how soils respond to rising temperatures is...

Discovering Ultra-Stable Metal-Organic Frameworks for CO Capture from A Wet Flue Gas: Integrating Machine Learning and Molecular Simulation.

Environmental science & technology
The rapid increase in atmospheric CO, arising from anthropogenic sources, has posed a severe threat to global climate and raised widespread environmental concern. Metal-organic frameworks (MOFs) are promising adsorbents to potentially reduce CO emiss...