Conductive films of silver nanoparticles as novel susceptors for induction welding of thermoplastic composites
Bibliographic record
Abstract
Abstract In the present work, a conductive film of silver nanoparticles (nAg) as a novel heating element type, called susceptor, was developed and tested for induction welding of carbon fiber/polyphenylene sulfide (CF/PPS) thermoplastic composites, i.e., unidirectional pre-impregnated 16 plies of CF/PPS compression-molded in a quasi-isotropic stacking sequence. The nAg were synthesized, dispersed in deionized (DI) water and casted onto a pure PPS film, resulting in a conductive film upon the evaporation of DI water and thermal post-annealing. The thermal annealing at 250 °C significantly (by 7 orders) decreased the film’s electrical resistivity from 9.4 × 10 3 down to 3.1 × 10 −4 Ω cm. The new susceptors led to fast heating rates in induction welding when compared to the standard stainless steel mesh susceptors under similar welding conditions. Lap shear mechanical testing revealed that the apparent lap shear strength (LSS) is sensitive to the susceptors’ resistivity and the input current. A relatively high LSS value was achieved for the specimens welded using the new susceptors which exceeded the value of those welded using stainless steel mesh susceptors (28.3 MPa compared to 20 MPa). The weld interface and specimens’ cross-section observation revealed that the nAg were dispersed and embedded into the resin upon welding. This study contains preliminary results that show high potential of nanoparticles as effective susceptors to further improve the mechanical performance of the joints in welding of thermoplastic composites.
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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 | 0.000 | 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 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".