(Invited) Synthesis and Electrochemical Study of Gold Based Nanomaterials
Bibliographic record
Abstract
Gold-based nanomaterials have received increasing attention in a wide range of energy, environmental and medical applications due to their unique physicochemical properties, chemical robustness, and distinct catalytic activities [1-5]. In this presentation, we mainly focus on the synthesis and modification of nanoporous Au as well as its promising energy, environmental and biological applications. The primary strategies for the fabrication of nanoporous gold, including chemical dealloying, electrochemical alloying/dealloying, and dynamic hydrogen bubble template approach, will be presented. The development of nanoporous gold based electrochemical sensors for the detection of biomarkers, contaminants, and food additives will be highlighted. In addition, the high catalytic activity of nanoporous gold towards the electrochemical reduction of carbon dioxide will be discussed. References [1] Z. Liu, A. Nemec-Bakk, N. Khaper, A. Chen, Anal. Chem. 89 (2017) 8036-8043 [2] V.S. Manikandan, B.-R. Adhikari, A. Chen, Analyst 143 (2018) 4537-4554 [3] M.N. Hossain, Z. Liu, J. Wen, A. Chen, Appl. Catal. B: Environ. 236 (2018) 483-489 [4] Z. Liu, V.S. Manikandan, A. Chen, Current Opin. Electrochem. 16 (2019) 127-133 [5] Z. Liu, E. Puumala, A. Chen, Sens. Actuators B: Chem. 287 (2019) 517-525.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".