Improvement of the Performance of Structural Adhesive Joints with Nanoparticles and Numerical Prediction of Their Response
Bibliographic record
Abstract
The use of nanoparticles as an effective means for improving the performance of adhesives and resins has attracted considerable attention immediately after the helical microtubules of graphitic carbon were introduced. Since then, several researchers have harnessed the outstanding mechanical properties of nanoparticles (NPs) to improve performances of adhesives and resins. As a result, the number of scholarly articles on this topic has also increased exponentially since the early 90s, with no plateau in the rate of scholarly publications in sight. Overall, a large majority of the available articles concerning the incorporation of NPs for improving the performance of adhesives and resins are experimentally oriented compared to a relatively much lower number of articles that examine the characterization of the performance of such composite materials both theoretically and/ or numerically. This chapter, therefore, aims at providing a summary of the recent advances concerning the use of nanoparticles for improving the performance of adhesively bonded joints (ABJs). The emphasis is placed on the articles that have explored the influences of environmental parameters that affect the performance of NP-reinforced ABJs. A review of the relevant numerical studies with a particular emphasis on the extended finite element method is also presented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".