Development of a Programmable Rastering Open-Source Electrodeposition System
Bibliographic record
Abstract
A prototype rasterizable electroplating system constructed entirely of open-source systems and off-the-shelf electronic components has been developed. The system as proposed can be broken down into separate movement and electroplating systems. The movement system consists of a Creality Ender 3 3D-Printer which has been modified to support the electroplating system. This electroplating system was built using an Arduino microcontroller that controls the voltage and current of the system. The system polarization can be controlled potentiostatically or galvanostatically in an operating window of 0 to 5V with currents up to 300 mA and a stability of 2% accuracy. The results presented will be a demonstration of the system operation in an electrolytic capacity, with simple water splitting in a bicarbonate solution chosen to examine the controls and system readings when electrodes are moved geometrically throughout a simple two electrode cell with unequal electrode shapes. A discussion of the set-up process for this system follows to elaborate on the challenges of translating STL file decoding to the path the printer must follow, which is then further sliced and translated to Gcode. Limitations to the Gcode generation and further modifications to allow for cross-communication between the Arduino and Ender control systems are discussed, highlighting the limitations of these economic yet powerful system builds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".