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Related Experiment Video

Updated: Jan 22, 2026

A Microfluidic Model of Biomimetically Breathing Pulmonary Acinar Airways

A Microfluidic Model of Biomimetically Breathing Pulmonary Acinar Airways

Published on: May 9, 2016

Computational Modeling for Biomimetic Sensors.

Icell M Sharafeldin¹, Jessica E Fitzgerald², Hicham Fenniri³

¹Energy Materials Laboratory, School of Sciences and Engineering, The American University in Cairo, New Cairo, Egypt.

Methods in Molecular Biology (Clifton, N.J.)

|July 17, 2019

Summary

This summary is machine-generated.

Computational modeling, particularly Density Functional Theory (DFT), aids scientists in predicting material properties and understanding mechanisms. Simultaneous computational and experimental approaches enhance accuracy for future predictions.

Keywords:

Barcoded polymer Computational modeling Density functional theory Multivariate data analysis

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Biomimetic Materials to Characterize Bacteria-host Interactions

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Published on: November 16, 2015

Area of Science:

Computational chemistry and materials science.
Application of theoretical methods to understand chemical systems.

Background:

Computational modeling is crucial for predicting material properties and elucidating mechanisms.
Density Functional Theory (DFT) is a key computational method.

Purpose of the Study:

To provide a holistic introduction to computational modeling, focusing on DFT methods.
To guide users through computational modeling basics using Gaussian16 software.
To explain structure building, calculation parameters, and result interpretation.

Main Methods:

Discussion of various computational modeling techniques: molecular mechanics, semiempirical, and ab initio methods.
Step-by-step guide using Gaussian16 software for computational modeling.
Explanation of basis sets and exchange-correlation functionals.

Main Results:

Demonstration of computational modeling applied to biomimetic polymer beads.
Focus on simulating Raman spectra and various solvation environments.
Exploration of simultaneous computational and experimental workflows.

Conclusions:

Computational modeling, especially DFT, is vital for scientific discovery.
Integrating computational and experimental data improves predictive accuracy.
The presented guide is broadly applicable to diverse computational modeling tasks.