Explanipedia

A physics-based data-driven model for CO$_2$ gas diffusion electrodes to drive automated laboratories Open

Ivan Grega, Félix Therrien, Abhishek Soni, Karry Ocean, Kevan E. Dettelbach , et al. · 2025

The electrochemical reduction of atmospheric CO$_2$ into high-energy molecules with renewable energy is a promising avenue for energy storage that can take advantage of existing infrastructure especially in areas where sustainable alternat…

Accelerated development of gas diffusion electrodes for CO2 electrolyzers Open

Abhishek Soni, Siwei Ma, Karry Ocean, Kevan E. Dettelbach, Daniel Lin , et al. · 2024

Here we present a high-throughput flexible automation system, AdaCarbon, to accelerate the development of gas diffusion electrodes (GDEs) for CO2 electrolysis. AdaCarbon consists of a team of seven robots with automated modules for GDE fab…

A self-driving laboratory optimizes a scalable process for making functional coatings Open

Connor C. Rupnow, Benjamin P. MacLeod, Mehrdad Mokhtari, Karry Ocean, Kevan E. Dettelbach , et al. · 2023

Functional coatings are used in a wide range of high surface-area technologies, such as low-E windows and photovoltaics. Solution-based coatings are typically less expensive to produce than vacuum-based coatings; however, it is generally m…

A self-driving laboratory advances the Pareto front for material properties Open

Benjamin P. MacLeod, Fraser G. L. Parlane, Connor C. Rupnow, Kevan E. Dettelbach, Michael S. Elliott , et al. · 2022

Useful materials must satisfy multiple objectives, where the optimization of one objective is often at the expense of another. The Pareto front reports the optimal trade-offs between these conflicting objectives. Here we use a self-driving…

Advancing the Pareto front for thin-film materials using a self-driving laboratory Open

Benjamin P. MacLeod, Fraser G. L. Parlane, Connor C. Rupnow, Kevan E. Dettelbach, Michael S. Elliott , et al. · 2021

Useful materials must satisfy multiple objectives, where the optimization of one objective is often at the expense of another. The Pareto front reports the optimal trade-offs between competing objectives. Here we report a self-driving labo…

Advancing the Pareto front for thin-film materials using a self-driving\n laboratory Open

Benjamin P. MacLeod, Fraser G. L. Parlane, Connor C. Rupnow, Kevan E. Dettelbach, Michael Scott Elliott , et al. · 2021

Useful materials must satisfy multiple objectives, where the optimization of\none objective is often at the expense of another. The Pareto front reports the\noptimal trade-offs between competing objectives. Here we report a self-driving\nl…

Modeling novel ionenes for electrochemical devices with first principles and machine learning methods Open

Mehrdad Mokhtari · 2020

Polybenzimidazole-based ionenes are being developed for use in both alkaline anion-exchange membrane fuel cells and alkaline polymer electrolysers. The first part of this work explores the impact of the degree of methylation on the conform…

Electronic Structure and Conformational Properties of Polybenzimidazole-Based Ionenes—A Density Functional Theory Investigation Open

Mehrdad Mokhtari, Mohammad J. Eslamibidgoli, Michael Eikerling · 2020

Polybenzimidazole-based ionenes are explored for use in both alkaline anion-exchange membrane fuel cells and alkaline polymer electrolyzers. Poly-(hexamethyl-p-terphenylbenzimidazolium) (HMT-PMBI), the material of interest in this a…

Recurrent Neural Network-based Model for Accelerated Trajectory Analysis in AIMD Simulations Open

Mohammad J. Eslamibidgoli, Mehrdad Mokhtari, Michael Eikerling · 2019

The presented work demonstrates the training of recurrent neural networks (RNNs) from distributions of atom coordinates in solid state structures that were obtained using ab initio molecular dynamics (AIMD) simulations. AIMD simulations on…

Recurrent Neural Network-based Model for Accelerated Trajectory Analysis\n in AIMD Simulations Open

Mohammad J. Eslamibidgoli, Mehrdad Mokhtari, Michael Eikerling · 2019

The presented work demonstrates the training of recurrent neural networks\n(RNNs) from distributions of atom coordinates in solid state structures that\nwere obtained using ab initio molecular dynamics (AIMD) simulations. AIMD\nsimulations…

Statistical Analysis on Preliminary Breakdown Pulses of Positive Cloud-to-Ground Lightning in Malaysia Open

Chin‐Leong Wooi, Zulkurnain Abdul‐Malek, Noor Azlinda Ahmad, Mehrdad Mokhtari, Behnam Salimi · 2016

This aim of this paper is to study the preliminary breakdown pulses (PBP) of positive cloud-to-ground (+CG) lightning flashes in Malaysia. There is a difference in PBP characteristics when a comparison is made between positive and negative…

Integration of Frequency Dependent Soil Electrical Properties in Grounding Electrode Circuit Model Open

Mehrdad Mokhtari, Zulkurnain Abdul‐Malek, Chin‐Leong Wooi · 2016

The effect of frequency dependent soil properties on the impedance and transient response of the grounding electrode was investigated. The frequency dependent soil models as proposed by Scott, Smith-Longmire, and Visacro-Alipio were …