Nickel Phthalocyanine: Borophene P-N Junction-Based Thermoelectric Generator
Bibliographic record
Abstract
In this study, borophene and nickel phthalocyanine (NiPc): borophene nanocomposites were prepared using the sonication method. The NiPc: borophene nanocomposite was uniformly obtained as a 10–80 nm-sized spherically shaped particle. Electrical conductivities (s) were measured as 3 × 10−13 Scm−1 and 9.5 × 10−9 Scm−1 for NiPc and the NiPc: borophene nanocomposite, respectively. The SEM image showed that borophene was homogeneously distributed in the NiPc matrix and increased the charge transport pathways. This is the main reason for a 106-fold increase in electrical conductivity. An indium tin oxide (ITO)/NiPc: borophene nanocomposite-based thermoelectric generator (TEG) was prepared and characterized. The Seebeck coefficients (S) were calculated to be 5 μVK−1 and 30 μVK−1 for NiPc and the NiPc: borophene nanocomposite, respectively. A positive Seebeck coefficient value for the NiPc: borophene showed the p-type nature of the nanocomposite. The power factors (PF = sS2) were calculated as 7.5 × 10−16 μW m−1 K−2 and 8.6 × 10−10 μW m−1 K−2 for NiPc and the NiPc: borophene nanocomposite, respectively. Compositing NiPc with borophene increased the power factor by ~106-fold. It has been concluded that the electrical conductivity and Seebeck coefficient of the NiPc: borophene material increases due to energy band convergence because of combining p-type NiPc with p-type borophene. Therefore, the NiPc: borophene nanocomposite is a promising material for TEG.
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 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 | 0.001 |
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".