Teesmat an Open Innovation Test Bed for Electrochemical Devices: Example of X-Ray Nano-Tomography As Characterization Tool for Battery Analysis
Bibliographic record
Abstract
The recent rapid increase of the demand of higher energy density along with higher power density lithium-ion batteries (LiBs) requires the development of advanced cathode/anode materials with higher capacity. These challenges can be addressed providing that powerful characterization tools are able to probe the degradation phenomena occurring at multiple scale. In this context, the TEESMAT platform aims to widen the access to a large panel of characterization techniques among different partners across Europe for solving industrial problematics in energy related field. Among these, X-ray tomography has been used as non-invasive 3D investigation tool that spread along a wide range of applications in order to probe at different length scale their microstructure. Moreover, phase contrast imaging has brought to light a practical way to enhance visibility between weak absorbing materials and/or small details of differing refractive index within structure, and thus accessing a sharpen overview of the 3D morphology of complex material, which is of particular interest in the frame of energy-related materials. This presentation will focus on various industrial case studies such as NMC-based and LFP-based all solid-state batteries, hybrid Li-ion supercapacitor and current collector manufacturing. The results presented are part of the TEESMAT project, which received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 814106.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".