An affordable platform for automated synthesis and electrochemical characterization
Bibliographic record
Abstract
Electrochemical techniques are pivotal for material discovery and renewable energy; however, often the extensive chemical spaces to be explored require high-throughput experimentation (HTE) to ensure timely results, which are costly both for instrument and materials/consumables. While self-driving laboratories (SDL) promise efficient chemical exploration, most contemporary implementations demand significant time, economic investment, and expertise. This study introduces an open and cost-effective autonomous electrochemical setup, comprising a synthesis platform and a custom-designed potentiostat device. We present an SDL platform that offers rapid deployment and extensive control over electrochemical experiments compared to commercial alternatives. Using ChemOS 2.0 for orchestration, we showcase our setup's capabilities through a campaign in which different metal ions reacts with ligands to form coordination compounds., yielding a database of 400 electrochemical measurements. Committed to open science, we provide a potentiostat design, campaign software, and raw data, aiming to democratize customized components in SDLs and ensure transparent data sharing and reproducibility.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".