1,721,080 research outputs found
Thermal transmittance in graphene based networks for polymer matrix composites
Full text linkGraphene nanoribbons (GNRs) can be added as fillers in polymer matrix composites for enhancing their thermo-mechanical properties. In the present study, we focus on the effect of chemical and geometrical characteristics of GNRs on the thermal conduction properties of composite materials. Configurations consisting of single and triple GNRs are here considered as representative building blocks of larger filler networks. In particular, GNRs with different length, relative orientation and number of cross-linkers are investigated. Based on results obtained by Reverse Non-equilibrium Molecular Dynamics simulations, we report correlations relating thermal conductivity and thermal boundary resistance of GNRs with their geometrical and chemical characteristics. These effects in turn affect the overall thermal transmittance of graphene based networks. In the broader context of effective medium theory, such results could be beneficial to predict the thermal transport properties of devices made of polymer matrix composites, which currently find application in energy, automotive, aerospace, electronics, sporting goods, and infrastructure industries
Bottom up approach toward prediction of effective thermophysical properties of carbon-based nanofluids
Full text linkCarbon-based nanofluids, mainly suspensions of carbon nanotubes or graphene sheets in water, are typically characterized by superior thermal and optical properties. However, their multiscale nature is slowing down the investigation of optimal geometrical, chemical, and physical nanoscale parameters for enhancing the thermal conductivity while limiting the viscosity increase at the same time. In this work, a bottom up approach is developed to systematically explore the thermophysical properties of carbon-based nanofluids with different characteristics. Prandtl number is suggested as the most adequate parameter for evaluating the best compromise between thermal conductivity and viscosity increases. By comparing the Prandtl number of nanofluids with different characteristics, promising overall performances (that is, nanofluid/base fluid Prandtl number ratios equal to 0.7) are observed for semidilute (volume fraction ⩽ 0.004) aqueous suspensions of carbon nanoparticles with extreme aspect ratios (larger than 100 for nanotubes, smaller than 0.01 for nanoplatelets) and limited defects concentrations (<5%). The bottom up approach discussed in this work may ease a more systematic exploration of carbon-based nanofluids for thermal applications, especially solar ones
Water transport control in carbon nanotube arrays
Full text linkBased on a recent scaling law of the water mobility under nanoconned conditions, we envision novel strategies for precise modulation of water diffusion within membranes made of Carbon Nanotube Arrays - CNAs. In a rst approach, the water diusion coecient D may be tuned by nely controlling the size distribution of the pore size. In the second approach, D can be varied at will by means of externally induced electrostatic fields. Starting from the latter strategy, switchable molecular sieves are proposed, where membranes are properly designed with sieving and permeation features that can be dynamically activated/deactivates. Areas where a precise control of water transport properties is benecial range from energy and environmental engineering up to nanomedicin
Inference of analytical thermodynamic models for biological networks
Full text linkWe present an automated algorithm for inferring analytical models of closed reactive biochemical mixtures, on the basis of standard approaches borrowed from thermodynamics and kinetic theory of gases. As an input, the method requires a number of steady states (i.e. an equilibria cloud in the phase-space), and at least one time series of measurements for each species. Validations are discussed for both the Michaelis-Menten mechanism (four species, two conservation laws) and the mitogen-activated protein kinase - MAPK - mechanism (eleven species, three conservation laws
Enhancing the ReaxFF DFT database
No full text<h1>Enhancing the ReaxFF DFT database</h1>
<p>This repository contains the database used to re-parametrize the ReaxFF force field for LiF, an inorganic compound. The purpose of the database is to improve the accuracy and reliability of ReaxFF calculations for LiF. The results and method used were published in the article <a href="https://doi.org/10.1038/s41598-023-50978-5">Enhancing ReaxFF for Molecular Dynamics Simulations of Lithium-Ion Batteries: An interactive reparameterization protocol</a>.</p>
<p>This database was made using the simulation obtained using the protocol published in <a href="https://github.com/paolodeangelis/Enhancing_ReaxFF">Enhancing ReaxFF repository</a>.</p>
<h2>Installation</h2>
<p>To use the database and interact with it, ensure that you have the following Python requirements installed:</p>
<p><strong>Minimum Requirements:</strong></p>
<ul>
<li>Python 3.9 or above</li>
<li>Atomic Simulation Environment (ASE) library</li>
<li>Jupyter Lab</li>
</ul>
<p><strong>Requirements for Re-running or Performing New Simulations:</strong></p>
<ul>
<li>SCM (Software for Chemistry & Materials) Amsterdam Modeling Suite</li>
<li>PLAMS (Python Library for Automating Molecular Simulation) library</li>
</ul>
<p>You can install the required Python packages using pip:</p>
<pre><code>pip install -r requirements.txt</code></pre>
<blockquote>
<p><strong>Warning</strong></p>
<p>Make sure to have the appropriate licenses and installations of SCM Amsterdam Modeling Suite and any other necessary software for running simulations.</p>
</blockquote>
<h2>Folder Structure</h2>
<p>The repository has the following folder structure:</p>
<pre><code>.
├── CONTRIBUTING.md
├── CREDITS.md
├── LICENSE
├── README.md
├── requirements.txt
├── assets
├── data
│ ├── LiF.db
│ ├── LiF.json
│ └── LiF.yaml
├── notebooks
│ ├── browsing_db.ipynb
│ └── running_simulation.ipynb
└── tools
├── db
├── plams_experimental
└── scripts</code></pre>
<ul>
<li><code>CONTRIBUTING.md</code>: This file provides guidelines and instructions for contributing to the repository. It outlines the contribution process, coding conventions, and other relevant information for potential contributors.</li>
<li><code>CREDITS.md</code>: This file acknowledges and credits the individuals or organizations that have contributed to the repository.</li>
<li><code>LICENSE</code>: This file contains the license information for the repository (CC BY 4.0). It specifies the terms and conditions under which the repository's contents are distributed and used.</li>
<li><code>README.md</code>: This file.</li>
<li><code>requirements.txt</code>: This file lists the required Python packages and their versions. (see <a href="#installation">installation section</a>)</li>
<li><code>assets</code>: This folder contains any additional assets, such as images or documentation, related to the repository.</li>
<li><code>data</code>: This folder contains the data files used in the repository.
<ul>
<li><code>LiF.db</code>: This file is the SQLite database file that includes the DFT data used for the ReaxFF force field. Specifically, it contains data related to the inorganic compound LiF.</li>
<li><code>LiF.json</code>: This file provides the database metadata in a human-readable format using JSON.</li>
<li><code>LiF.yaml</code>: This file also contains the database metadata in a more human-readable format, still using YAML.</li>
</ul>
</li>
<li><code>notebooks</code>: This folder contains Jupyter notebooks that provide demonstrations and examples of how to use and analyze the database.
<ul>
<li><code>browsing_db.ipynb</code>: This notebook demonstrates how to handle, select, read, and understand the data points in the <code>LiF.db</code> database using the ASE database Python interface. It serves as a guide for exploring and navigating the database effectively.</li>
<li><code>running_simulation.ipynb</code>: In this notebook, you will find an example of how to get a data point from the <code>LiF.db</code> database and use it to perform a new simulation. The notebook showcases how to utilize either the <a href="https://www.scm.com/doc/plams/index.html">PLAMS</a> library or the <a href="https://www.scm.com/doc/plams/interfaces/amscalculator.html">AMSCalculator</a> and ASE Python library to conduct simulations based on the retrieved data and then store it as a new data point in the <code>LiF.db</code> database. It provides step-by-step instructions and code snippets for a seamless simulation workflow.</li>
</ul>
</li>
<li><code>tools</code>: This directory contains a collection of Python modules and scripts that are useful for reading, analyzing, and re-running simulations stored in the database. These tools are indispensable for ensuring that this repository adheres to the principles of <strong>I</strong>nteroperability and <strong>R</strong>eusability, as outlined by the <a href="https://www.go-fair.org/fair-principles/">FAIR principles</a>.
<ul>
<li><code>db</code>: This Python module provides functionalities for handling, reading, and storing data in the database.</li>
<li><code>plasm_experimental</code>: This Python module includes the necessary components for using the <code>AMSCalculator</code> with PLASM and the SCM software package, utilizing the ASE API. It facilitates running simulations, and performing calculations.</li>
<li><code>scripts</code>: This directory contains additional scripts for advanced usage scenarios of this repository.</li>
</ul>
</li>
</ul>
<h2>Interacting with the Database</h2>
<p>There are three ways to interact with the database: using the ASE db command line, the web interface, and the ASE Python interface.</p>
<h3>ASE db Command-line</h3>
<p>To interact with the database using the ASE db terminal command, follow these steps:</p>
<ol>
<li>
<p>Open a terminal and navigate to the directory containing the <code>LiF.db</code> file.</p>
</li>
<li>
<p>Run the following command to start the ASE db terminal:</p>
<pre><code>ase db LiF.db</code></pre>
</li>
<li>
<p>You can now use the available commands in the terminal to query and manipulate the database. More information can be found in the <a href="https://wiki.fysik.dtu.dk/ase/ase/db/db.html">ASE database documentation</a>.</p>
</li>
</ol>
<h3>Web Interface</h3>
<p>To interact with the database using the web interface, follow these steps:</p>
<ol>
<li>
<p>Open a terminal and navigate to the directory containing the <code>LiF.db</code> file.</p>
</li>
<li>
<p>Run the following command to start the ASE db terminal:</p>
<pre><code>ase db -w LiF.db</code></pre>
</li>
<li>
<p>Open your web browser and connect to the local server at <a href="http://127.0.0.1:5000">http://127.0.0.1:5000</a>.</p>
</li>
</ol>
<blockquote>
<p><strong>Warning</strong></p>
<p>To visualize the 3D structure of the system, you need to install the <a href="https://jmol.sourceforge.net/">JMOL extension</a>. You can use the script <code>tools/scripts/install_jmol.py</code> to automatically download and install it:</p>
<pre><code>cd tools/scripts/
python install_jmol.py</code></pre>
</blockquote>
<h3>ASE Python Interface</h3>
<p>To interact with the database using the ASE Python interface, you can use the following example code:</p>
<pre><code>from ase.db import connect
# Connect to the database
db = connect("LiF.db")
# Query the database
results = db.select("success=True")
# Iterate over the results
for row in results:
print(f"ID: {row.id}, Energy: {row.energy}")</code></pre>
<div>
<pre>For a more detailed example, refer to the notebook <code>notebooks/browsing_db.ipynb</code>. To learn how to perform a simulation, check the notebook <code>notebooks/running_simulation.ipynb</code>.</pre>
</div>
<h2>Contributing</h2>
<p>If you would like to contribute to the Enhancing ReaxFF DFT Database by performing new simulations and expanding the database, please follow the guidelines outlined in the <a href="CONTRIBUTING.md">Contribution Guidelines</a>. You are welcome to submit pull requests or open issues in the repository. Your contributions are greatly appreciated!</p>
<h2>How to Cite</h2>
<p>If you use the database or the tools provided in this repository for your work, please cite it using the following BibTeX entries:</p>
<pre><code>@article{deangelis2023enhancing,
title={Enhancing ReaxFF for molecular dynamics simulations of lithium-ion batteries: an interactive reparameterization protocol},
author={De Angelis, Paolo and
Cappabianca, Roberta and
Fasano, Matteo and
Asinari, Pietro and
Chiavazzo, Eliodoro},
journal={Scientific Reports},
volume={14},
number={1},
pages={978},
year={2024},
publisher={Nature Publishing Group UK London}
}</code></pre>
<pre><code>@dataset{EnhReaxFFdatabase,
author = {De Angelis, Paolo and
Cappabianca, Roberta and
Fasano, Matteo and
Asinari, Pietro and
Chiavazzo, Eliodoro},
title = {{Enhancing the ReaxFF DFT database}},
month = may,
year = 2023,
publisher = {Zenodo},
version = {1.0.0-beta},
doi = {10.5072/zenodo.1204707},
url = {https://doi.org/10.5281/zenodo.7959121}
}</code></pre>
<div>
<h2>License</h2>
</div>
<p>The contents of this repository are licensed under the <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p>
<h2>Acknowledgments</h2>
<p>This project has received funding from the European Union's <a href="https://ec.europa.eu/programmes/horizon2020/en">Horizon 2020 research and innovation programme</a> under grant agreement <a href="https://cordis.europa.eu/project/id/957189">No 957189</a>. The project is part of <a href="https://battery2030.eu/">BATTERY 2030+</a>, the large-scale European research initiative for inventing the sustainable batteries of the future.</p>
<p>The authors also acknowledge that the simulation results of this database have been achieved using the <a href="https://prace-ri.eu/hpc-access/deci-access/">DECI</a> resource <a href="https://www.archer2.ac.uk/">ARCHER2</a> based in UK at <a href="https://www.epcc.ed.ac.uk/">EPCC</a> with support from the <a href="https://prace-ri.eu/">PRACE</a> aisbl.</p>
Heat and mass transfer phenomena at solid-liquid nanoscale interface in theranostic applications
No full textNanoparticles show great potential for several biomedical applications. In particular, Superparamagnetic Iron Oxides (SPIOs) have attracted strong interest because of their theranostic features. SPIOs are both excellent T2 Magnetic Resonance Imaging (MRI) contrast agents and nanoparticles for thermal treatments, due to their capability to dissipate thermal energy if excited by alternating magnetic field. However, the understanding of heat and mass transfer phenomena at solid-liquid nanoscale interface is critical for designing specific theranostic particles. For instance, water dynamics is strongly affected by the proximity to solid surfaces, whose nature plays a major role both on the water mobility (hence on the contrast performances of MRI agents), and on the effective thermal transmittance, affecting the localized heat deployment in hyperthermic treatments. In this study, self-diffusion properties of nanoconfined water have been investigated using equilibrium Molecular Dynamics (MD). In particular, a scaling behavior of water self-diffusion coefficient is demonstrated, where a relevant dimensionless variable (expressing the ratio between confined and total water volumes) is suggested for describing water self-diffusivity in various nanoconfined configurations. In addition, non-equilibrium MD computations are performed for predicting temperature relaxation and consequentially for estimating solid/liquid interface thermal resistance, which is known to be the bottleneck in solid/liquid phononic heat conduction. Results show that both particle geometry and non-bonded interactions at the interface modulate heat diffusion in this region. Hence, a few guidelines for a priori design of novel nanoparticles for theranostic purposes are here outlined, based on atomistic simulations and thermodynamics of water under nanoconfined condition
Mesoscopic Modeling of Bio-Compatible PLGA Polymers with Coarse-Grained Molecular Dynamics Simulations
Full text linkA challenging topic in materials engineering is the development of numerical models that can accurately predict material properties with atomistic accuracy, matching the scale and level of detail achieved by experiments. In this regard, coarse-grained (CG) molecular dynamics (MD) simulations are a popular method for achieving this goal. Despite the efforts of the scientific community, a reliable CG model with quasi-atomistic accuracy has not yet been fully achieved for the design and prototyping of materials, especially polymers. In this paper, we describe a CG model for polymers, focusing on the biocompatible poly(lactic-co-glycolic acid) (PLGA), based on a general parametrization strategy with a potentially broader field of applications. In this model, polymers are represented with finite-size ellipsoids, short-range interactions are accounted for with the generalized Gay-Berne potential, while electrostatic and long-range interactions are accounted for with point charges within the ellipsoids. The model was validated against its atomistic counterpart, obtained through a back-mapping process, by comparing physical properties such as glass transition temperature, thermal conductivity, and elastic moduli. We observed quantitative agreement between the atomistic and CG representations, thus opening up the possibility of adopting the proposed model to expand the domain size of typical MD simulations to dimensions comparable to those of experimental setups
Calcium alginate hydrogel for high-yield adsorption-based desalination driven by ultralow-grade heat
No full textAdsorbent-based desalination stands out as a promising solution to produce fresh water leveraging on ultralow-grade heat. We investigate the use of a bio-derived calcium alginate (CaAlg) hydrogel, an alternative to other common sorbents. A batch of CaAlg is synthesized and characterized in a gravimetric sorption analyzer. The resulting type II isotherm at 30 degrees C shows an exceptional water uptake equilibrium value of 1.28 g/g at a relative humidity of 70%, realizing nearly a 4-fold increase compared to standard silica gels under the same conditions. We test CaAlg under vacuum in a water desalination unit. Our material shows an excellent medium-term cyclability, with a stable uptake over 40 cycles of operation. We achieve water production even at a temperature as low as 45 degrees Celsius, with a specific daily water production (SDWP) of 6 m3/day/ton using a hot-source temperature of 60 degrees Celsius, realizing the most performant small-scale system in the context of ultralow-grade waste heat valorization
Towards a multiscale simulation approach of nanofluids for volumetric solar receivers: Assessing inter-particle potential energy
Full text linkA modern concept for solar thermal collectors is based on volumetric absorption of sunlight, where nanoparticles suspended in liquids directly receive the incident radiation. Suspending nanoparticles in traditional fluids can drastically enhance their optical properties and improve thermo-physical performances, thus leading to highly efficient volumetric solar receivers. Several studies have been addressed on the physical understanding of such nanosuspensions; however, the relation between nanoscale effects and macroscopic properties is far from being fully understood. The present work represents a first step towards a multiscale modeling approach for relating nanoscale properties to macroscopic behaviour of nanofluids. In particular, a suitable Coarse-Grained (CG) method for nanofluids is described. By means of Molecular Dynamics (MD) simulations, the pair Potential of Mean Forces (pPMF) between CG beads of nanofluid is evaluated. A complete CG force field can be then defined by including the effects of water adsorbed at solid-liquid interface, nanoparticle surface charge and solution pH. Our multiscale model is intended to permit a future study of the complex mechanisms of nanoparticle clustering, which is known to affect nanofluids stability and properties. We hope that this multiscale approach may start the process of rational design of nanofluids thus facilitating technology transfer from lab experiments to large-scale industrial production
Passive solar high-yield seawater desalination by modular and low-cost distillation
No full textAlthough seawater is abundant, desalination is energy intensive and expensive. Using the Sun as an energy source is attractive for desalinating seawater. Although interesting, current passive devices with no moving parts have unsatisfactory performance when operated with an energy flux lower than 1 kW m−2 (one sun). We present a passive multi-stage and low-cost solar distiller, where efficient energy management leads to significant enhancement in freshwater yield. Each unit stage for complete distillation is made of two hydrophilic layers separated by a hydrophobic microporous membrane, with no other mechanical ancillaries. Under realistic conditions, we demonstrate a distillate flow rate of almost 3 l m−2 h−1 from seawater at less than one sun—twice the yield of recent passive complete distillation systems. Theoretical models also suggest that the concept has the potential to further double the observed distillate rate. In perspective, this system may help satisfy the freshwater needs in isolated and impoverished communities in a sustainable way
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