Investigating the Mechanical and Thermal Properties of Concrete with Recycled Nanoplastics for Enhanced Sustainability
Bibliographic record
Abstract
Over recent decades, the concrete industry has experienced significant advancements, including the incorporation of recycled waste materials to enhance sustainability and various properties.However, the current literature lacks comprehensive investigations regarding the critical contributions and benefits of Nanoplastics (NPs) in improving concrete's mechanical and thermal properties, such as tensile strength, compressive strength, splitting strength, and thermal characteristics, including thermal stability and insulation.This study aims to investigate the valuable effects of integrating waste materials, particularly NPs, into concrete and to elucidate the impact of NPs on concrete's mechanical and thermal properties.Furthermore, this paper seeks to highlight the positive environmental implications of using NPs in concrete.A comprehensive literature review was conducted to address these aspects and achieve the research objectives.Academic peer-reviewed articles and recently published papers were examined to discuss the practicality and various essential benefits of incorporating NPs into the concrete mix.The review revealed that the use of NPs in concrete leads to improvements in mechanical properties, such as shrinkage, splitting tensile strength, compressive strength, shear resistance, and flexural strength.Simultaneously, the substitution of various NP ratios in concrete enhances thermal resistance, contributing to more energy-efficient structures due to the substantial thermal insulation provided by the plastic incorporation.With respect to environmental aspects, employing waste materials renders the concrete more sustainable, eco-friendly, and cost-effective compared to conventional concrete.Based on the thorough review conducted, which demonstrated the advantageous effects of NPs on concrete performance concerning mechanical, thermal, and ecological measures, it is crucial to expand the available literature on the beneficial evaluations of NPs' contributions to the efficacy and practicality of the concrete mix.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".