Bibliographic record
Abstract
Additive manufacturing (AM) is quickly leading a new revolution in manufacturing. Aerosol ink jet printing (AJP) is a non-contact printing method that allows for printing on irregular substrates. When paired nanoparticulate ink, the method can print electrical traces and sensors. AJP stands to surpass current thin film technologies by flexibly printing on complex geometries. This thesis details the preliminary work towards employing AJP to create sensors operating in harsh environments. Specifically, the development of materials required to enable printed circuits functioning at temperatures exceeding 1000˚C (1850 ˚F). \nThe high temperature corrosion behavior of devices created from nanoparticles is explored from starting with the synthesis of the nanoparticles themselves. Inks suitable for AJP are formed from the nanoparticles. The inks are subsequently printed into strain gauge designs, sintered to bulk, and tested for conductivity. A technique to create core shell nanoparticles is demonstrated in efforts to make the ink materials more resistant to side reactions during the sintering phase. \nAn additional design aspect is introduced in the form of sol gels to solve the corrosion challenges presented. Sol gels were developed to create ceramic thin films to insulate the manufactured sensors, provide an engineered surface, and encapsulation layer for the devices. Sol gel chemistry is a wet chemical approach for forming ceramics that is also found to be compatible with AJP processes. \nOnly a few sensors produced were suitable for electrical characterization. This was due to side reactions in the sintering process as well as insufficient adhesion of the printed traces to the substrate. The resistive path of the sensor was 31 kohms, which was outside of the testing range for strain gauges. The elevated resistance of these samples is due to impurities and defects in the printed patterns. \nThe findings of this thesis are useful for generating the next generation devices for use in harsh environments. The materials established here can be altered by differing processing techniques to eliminate the barriers to achieving integrated strain gauges by additive manufacturing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".