Evidence map›Paper›PMID 37774155›Full record

ArticleACS nano2023

Nanoscale Prediction of the Thermal, Mechanical, and Transport Properties of Hydrated Clay on 10

Xiaojin Zheng, Ian C Bourg

Abstract read
In one paragraph

Article in ACS nano, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Xiaojin ZhengDepartment of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey 08544, United States.ORCID 0000-0001-6310-0162
Ian C BourgDepartment of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey 08544, United States.ORCID 0000-0002-5265-7229

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coupled thermal, hydraulic, mechanical, and chemical (THMC) processes, such as desiccation-driven cracking or chemically driven fluid flow, significantly impact the performance of composite materials formed by fluid-mediated nanoparticle assembly, including energy storage materials, ordinary Portland cement, bioinorganic nanocomposites, liquid crystals, and engineered clay barriers used in the isolation of hazardous wastes. These couplings are particularly important in the isolation of high-level radioactive waste (HLRW), where heat generated by radioactive decay can drive the temperature up to at least 373 K in the engineered barrier. Here, we use large-scale all-atom molecular dynamics simulations of hydrated smectite clay nanoparticle assemblages to predict the fundamental THMC properties of hydrated compacted clay over a wide range of temperatures (up to 373 K) and dry densities relevant to HLRW management. Equilibrium simulations of clay-water mixtures at different hydration levels are analyzed to quantify material properties, including thermal conductivity, heat capacity, thermal expansion, suction, water and ion self-diffusivity, and hydraulic conductivity. Predictions are validated against experimental results for the properties of compacted bentonite clay. Our results demonstrate the feasibility of using atomistic-level simulations of assemblages of clay nanoparticles on scales of tens of nanometers and nanoseconds to infer the properties of compacted bentonite on scales of centimeters and days, a direct upscaling over 6 orders of magnitude in space and 15 orders of magnitude in time.

Indexed as

bentonite barrierclay dehydrationmolecular dynamics simulationnanopore waterradioactive waste isolationTHMC properties

Identifiers

PMID37774155
PMCPMC10569101

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.