Direct and Plasmonic Nanoparticle‐Mediated Infrared Neural Stimulation: Comprehensive Computational Modeling and Validation
Bibliographic record
Abstract
Abstract Infrared neural stimulation techniques have potential applications in the diagnosis and treatment of numerous neurological and psychiatric disorders. There has been little progress in the computational modeling of these techniques and further improvement is needed in this area. In this paper, a comprehensive computational model is presented for simulating the complete mechanism of direct and plasmonic nanoparticle‐mediated infrared neural stimulation techniques in schematic samples of experimental setups. The simulation process involves three phases: 1) Simulating the light transmission and absorption in setups containing pure water or a gold nanorod solution using developed 3D, time‐independent, and time‐dependent Monte Carlo models, 2) calculating the spatiotemporal evolutions of temperature within the setup using the finite difference method and a presented novel method, and 3) simulating the thermally induced responses of lipid membranes using an improved method compared to existing theoretical models. The model is validated by comparing the computational results with existing experimental data. The effect of the laser pulse characteristics, nanofluid properties, and some other related parameters on the thermally induced membrane responses is investigated. The computational results help to optimize the parameters selection and maximize the overall efficiency of the infrared neural stimulation techniques.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".