Facile Deep Brain Electrode Coating with MXene for Improved Electrode Performance
Bibliographic record
Abstract
Abstract Accurate brain signal recording and precise electrode placement are critical for the success of neuromodulation therapies such as deep brain stimulation (DBS). Addressing these challenges requires deep brain electrodes that provide high‐quality, stable recordings while remaining compatible with high‐resolution medical imaging modalities like magnetic resonance imaging (MRI). Moreover, such electrodes shall be cost‐effective, easy to manufacture, and patient‐compatible. In this study, a facile dip‐coating approach is proposed using conductive titanium carbide (Ti 3 C 2 T x ) MXene nanosheets to enhance the performance of commercially available carbon fiber electrodes for chronic neural recording. Ti 3 C 2 T x ‐coated electrodes exhibit improved electrical conductivity, environmental and mechanical stabilities, reduced and stable impedance, and enhanced charge storage and injection capacity compared to uncoated carbon electrodes. When implanted in the rat dorsal hippocampal CA1 region, Ti 3 C 2 T x electrodes exhibited significantly lower impedance over 4 weeks, reduced susceptibility to 60 Hz line noise, and the capability to detect single‐unit neuronal activity−features not observed in uncoated controls. Notably, the Ti 3 C 2 T x coating does not induce inflammation at the implantation sites, and remained fully MRI‐compatible, unlike tungsten electrodes. These findings offer a straightforward and practical solution for achieving high‐quality chronic deep brain electrophysiology recordings while maintaining biocompatibility, safety, cost‐effectiveness, and MRI compatibility.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".