Photothermal Boost of Laser‐Synthesized TiC Colloidal Variants with Engineered Solid‐State Interfaces and Nitridation
Bibliographic record
Abstract
Colloids are vital in many fields, including optics, energy conversion, and photothermal therapy. TiC colloids and their variants are synthesized in acetonitrile using a "green" femtosecond laser fragmentation method under varying laser-power conditions. Three distinct types of TiC colloids are produced: i) pure TiC spherical nanoparticles at low laser power (<0.5 W) through thermodynamically driven spheroidization; ii) TiC nanoparticles with a multilayer graphene shell at moderate laser power (0.5-1 W) via carbon nucleation from acetonitrile decomposition; and iii) nitridated TiC nanoparticles with a multilayer graphene shell at high laser power (1.5-2 W), achieved through nitrogen incorporation facilitated by the breakdown of strong C≡N bonds in acetonitrile. The structural integrity of TiC in all three types of nanoparticles is maintained. The nanoparticle formation mechanisms are investigated, along with the effects of the interfaces and nitridation of TiC on its light-to-heat conversion. Photothermal measurements show a significant improvement in light-to-heat conversion for TiC nanoparticles with shells compared to pure TiC. This enhancement is attributed to light trapping within the colloidal solution, caused by the refractive index mismatch between TiC and graphene. Comparative scattering, absorption, and extinction efficiencies calculations based on Mie theory confirm this effect. Further photothermal enhancement is observed in the nitridated nanoparticles attributed to superior absorption in the NIR region. This study presents a sustainable approach for synthesizing photothermally efficient TiC-based colloids with nanoscale interface engineering and doping, enabling applications in energy and biomedical technologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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".