Ir-Ni oxide as a promising material for nerve and brain stimulating electrodes
Bibliographic record
Abstract
Tremendous potential for successful medical device development lies in both electrical stimulation therapies and neuronal prosthetic devices, which can be utilized in an extensive number of neurological disorders.These technologies rely on the successful electrical stimulation of biological tissue (i.e.neurons) through the use of electrodes.However, this technology faces the principal problem of poor stimulus selectivity due to the currently available electrode's large size relative to its targeted population of neurons.Irreversible damage to both the stimulated tissue and electrode are limiting factors in miniaturization of this technology, as charge density increases with decreasing electrode size.In an attempt to find an equilibrium between these two opposing constraints (electrode size and charge density), the objective of this work was to develop a novel iridium-nickel oxide (Ir 0.2 -Ni 0.8 -oxide) coating that could intrinsically offer high charge storage capacity.Thermal decomposition was used to fabricate titanium oxide, iridium oxide, nickel oxide, and bimetallic iridium-nickel oxide coatings on titanium electrode substrates.The Ir 0.2 -Ni 0.8 -oxide coating yielded the highest intrinsic (material property) and extrinsic (material property + surface area) charge storage capacity (CSC) among the investigated materials, exceeding the performance of the current state-ofthe-art neural stimulating electrode, Ir-oxide.This indicates that the Ir 0.2 -Ni 0.8 -oxide material is a promising alternative to currently used Ir-oxide, Pt, Au and carbon-based stimulating electrodes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".