Bibliographic record
Abstract
Broadening photon absorption into the near infrared (NIR) range represents a unique opportunity of improving the use of solar energy in various fields, including but not limited to solar fuel, solar cells and solar-enabled photo-degradation of pollutants. It is however challenging to realize this goal as most of traditional materials, such as dyes and semiconductors, do not absorb NIR lights efficiently. Recent advances of nanomaterials opens a new door in this research theme. In this talk, I will present some of our recent work on the design and synthesis of NIR absorbing materials, such as upconverting nanoparticles, plasmonic nanostructure with strong NIR plasmons, NIR absorbing quantum dots and two-dimensional black phosphorous, and on their combination with semiconductors for extending photon absorption into the NIR range for application in solar fuel and photocatalysis [1-8]. Rational design of hybrid nanomaterials, which is the key to maximize the benefits from respective nano-components, is highlighted. References: Am. Chem. Soc., 2013, 135, 9616; 2. Adv. Funct. Mater., 2019, under review; 3. Adv. Energy Mater. 2018, 1703658; 4. Adv. Funct. Mater. 2018, 1706235; 5. ACS Catalysis, 2017, 7, 6225; 6. Adv. Funct. Mater, 2015, 25, 2950; 7. Adv. Funct. Mater. 2015, 25, 6650; 8. Nanoscale Horizons, 2019, DOI: 10.1039/C8NH00373D
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".