Joining of Silver Nanomaterials at Low Temperatures: Processes, Properties, and Applications
Bibliographic record
Abstract
A review is provided, which first considers low-temperature diffusion bonding with silver nanomaterials as filler materials via thermal sintering for microelectronic applications, and then other recent innovations in low-temperature joining are discussed. The theoretical background and transition of applications from micro to nanoparticle (NP) pastes based on joining using silver filler materials and nanojoining mechanisms are elucidated. The mechanical and electrical properties of sintered silver nanomaterial joints at low temperatures are discussed in terms of the key influencing factors, such as porosity and coverage of substrates, parameters for the sintering processes, and the size and shape of nanomaterials. Further, the use of sintered silver nanomaterials for printable electronics and as robust surface-enhanced Raman spectroscopy substrates by exploiting their optical properties is also considered. Other low-temperature nanojoining strategies such as optical welding of silver nanowires (NWs) through a plasmonic heating effect by visible light irradiation, ultrafast laser nanojoining, and ion-activated joining of silver NPs using ionic solvents are also summarized. In addition, pressure-driven joining of silver NWs with large plastic deformation and self-joining of gold or silver NWs via oriented attachment of clean and activated surfaces are summarized. Finally, at the end of this review, the future outlook for joining applications with silver nanomaterials is explored.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".