<i>In situ</i>jet electrolyte micromachining and additive manufacturing
Bibliographic record
Abstract
Jet electrolyte micromachining (JEMM) exploits water-jet-assisted electrochemistry to achieve metal processing with spatial localization, precision, and flexibility. Currently, JEMM enables both micromilling and deposition, with the manufacturing precision and efficiency limited by the preparation and installation of the microscale tool electrodes (typically > 100 μm). Here, we develop a facile and low-cost platform for integrated in situ micro-subtractive and additive JEMM. Our technology is capable of machining micrometric grooves and pillars with controllable length scales (>20 μm) and topologies (patterns or spatial geometries) on metallic substrates. The integrated platform pumps electrolyte toward a workpiece through a nozzle to perform multiple tasks on the same setup, including micronozzle tool preparation, subtractive manufacturing, and additive manufacturing. We achieve this by controlling electrode polarity and electrolyte. We demonstrate our platform for microfabrication of grooves having a variety of widths ranging from 20 to 100 μm when working in the subtractive JEMM mode. In the additive JEMM mode, we demonstrate the fabrication of complex three-dimensional high-aspect-ratio micropillars having customized geometries beyond what is currently available with conventional methods. The proposed technology enables precise, controllable, efficient, and scalable additive and subtractive micromanufacturing for a plethora of applications.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".