A Comprehensive Review of Nanoparticle Incorporation in Construction and Architecture Materials: Impacts on Properties, Performance, and Sustainability
Bibliographic record
Abstract
Over the past two decades, there has been a rapid growth of research on nanotechnology in construction materials. This study presents a timely and comprehensive review that focuses on investigating the effects of incorporating various nanoparticles into cementitious, polymeric, and composite materials commonly used in the construction and architecture sectors. The primary objective is to critically analyze the potential benefits and limitations associated with the addition of nanoparticles, particularly in enhancing mechanical performance, durability, functionality, and sustainability. The study methodology involves an extensive analysis of published literature on nanoparticles applied in construction materials. The impact of nanomaterials on properties, including compressive strength, fracture toughness, stiffness, self-sensing capability, resistance to environmental degradation, antimicrobial effects, and recyclability is thoroughly examined. The findings reveal significant progress in demonstrating the capabilities of nanomaterials in tailoring the properties of cementitious composites, coatings, and plastics. However, challenges persist in such areas as dispersion, agglomeration, predicting long-term performance, toxicity evaluation, and feasibility assessment. Recommendations are provided, which focus on evaluating durability under in-service conditions, developing sustainable manufacturing methods, and establishing standardized protocols for material preparation and testing. The outcomes emphasize the need for a holistic approach that considers technical, environmental, economic, and social factors to facilitate the widespread adoption of nano-engineered materials. This comprehensive review serves as a valuable reference for researchers, engineers, architects, and construction professionals interested in understanding the current state-of-the-art, limitations, and future outlook on the integration of nanoparticles in construction applications.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".