Supercritical water gasification (SCWG) of lignocellulosic biomass is a promising pathway for the production of hydrogen. However, SCWG is a complex thermochemical process, the modeling of which is challenging via conventional methodologies. Therefor...
This study innovates plasmonic hydrogen sensors (PHSs) by applying phase space reconstruction (PSR) and convolutional neural networks (CNNs), overcoming previous predictive and sensing limitations. Utilizing a low-cost and efficient colloidal lithogr...
The main aim of the present study was to establish a relationship model between bio-hydrogen yield and the key operating parameters affecting photo-fermentation hydrogen production (PFHP) from co-substrates. Central composite design-response surface ...
Hydrogen can be produced in an environmentally friendly manner through biological processes using a variety of organic waste and biomass as feedstock. However, the complexity of biological processes limits their predictability and reliability, which ...
Physical chemistry chemical physics : PCCP
Nov 18, 2022
Exploring the structure and properties of molecular clusters with accuracy using the methods is a resource intensive task due to the increasing cost of the methods and the number of distinct conformers as the size increases. The energy landscape of...
The journal of physical chemistry letters
Oct 19, 2022
We have trained the Extreme Minimum Learning Machine (EMLM) machine learning model to predict chemical potentials of individual conformers of multifunctional organic compounds containing carbon, hydrogen, and oxygen. The model is able to predict chem...
Physical chemistry chemical physics : PCCP
Jun 1, 2022
In this study, a total of 302 molecular structures of phenylnaphthylamine antioxidants based on -phenyl-1-naphthylamine and -phenyl-2-naphthylamine skeletons with various substituents were modeled by exhaustive methods. Antioxidant parameters, includ...
Nanoclusters add an additional dimension in which to look for promising catalyst candidates, since catalytic activity of materials often changes at the nanoscale. However, the large search space of relevant atomic sites exacerbates the challenge for ...
This study aims to analyze and model cathodic H recovery (r), coulombic efficiency (CE) with inputs of voltage, electrical conductivity (EC) and anode potential, and H production rate and total energy recovery with inputs of r and CE in a microbial e...
Physical chemistry chemical physics : PCCP
Jun 1, 2020
Chemical representations derived from deep learning are emerging as a powerful tool in areas such as drug discovery and materials innovation. Currently, this methodology has three major limitations - the cost of representation generation, risk of inh...
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