Designs of Experiments for Beginners—A Quick Start Guide for Application to Electrode Formulation
Bibliographic record
Abstract
In this paper, we will describe in detail the setting up of a Design of Experiments (DoE) applied to the formulation of electrodes for Li-ion batteries. We will show that, with software guidance, Designs of Experiments are simple yet extremely useful statistical tools to set up and embrace. An Optimal Combined Design was used to identify influential factors and pinpoint the optimal formulation, according to the projected use. Our methodology follows an eight-step workflow adapted from the literature. Once the study objectives are clearly identified, it is necessary to consider the time, cost, and complexity of an experiment before choosing the responses that best describe the system, as well as the factors to vary. By strategically selecting the mixtures to be characterized, it is possible to minimize the number of experiments, and obtain a statistically relevant empirical equation which links responses and design factors.
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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 | 0.037 | 0.059 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.051 | 0.032 |
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".