Bibliographic record
Abstract
Carbon nanotubes (CNTs) discovered at 1991 have attracted great interest for applications in Nano-Electro-Mechanical-Systems (NEMS). However, the search for methods to join CNTs with metallic parts has been a worldwide challenge. Many efforts have been devoted to manipulating individual CNTs and joining them to each other. Joining processes so far attempted are premature and fall short of efficiency for joint quality evaluation. Thus, it has been found necessary to work on macro CNTs strands which are easy to handle via macro joining techniques. In this study, vacuum brazing technology has been developed for joining macro CNTs strands with Ni using a Ti-Ag-Cu alloy. The brazing mechanism has been confirmed as due to TiC formation at the CNTs/Ti-Ag-Cu interface. \n To evaluate this novel vacuum brazing technique for CNTs joining, the temperature effect on the brazing mechanism, microstructure and stoichiometry at joint interface needed to be understood. Firstly, the influence of temperature (from room temperature to 1000C) on mechanical behaviour of CNTs was well examined. The ultimate tensile strength (UTS) of CNTs was measured to be a maximum at 900C. \nThen, the mechanical performance of the joints was investigated from 850C to 1000C, and the fracture modes of the joints were identified. The UTS of joint also achieves maximum at 900C. Below 900C, due to little TiC formation, the bonding is weak thus leading to interfacial fracture. Above 900C, due to much TiC formation, the bonding is strong thus resulting in CNTs fracture. \n Furthermore, the vacuum brazing technique was applied to join CNTs to Ni contact wires used as a lamp filament. Compared to the filament joined by Ag paste or mechanical connection, the illumination of the brazed CNTs filament was stronger. The current density of the brazed filament was superior to the Ag paste connected filament. This may represent a promising way to produce energy saving lamps.
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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) | 0.003 | 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".