Laboratory Tests on Mercury Emission Monitoring with Resonating Gold-coated Silicon Cantilevers
Bibliographic record
Abstract
To measure extremely low concentrations of mercury vapor in gases as encountered in flue gases of coal-fired power plants, accurate and reliable online and/or portable mercury detection systems are needed. As discussed in this communication, resonating silicon-based cantilevers coated with thin films of gold change their resonant frequency when exposed to mercury vapors and could serve as the basis for such sensing devices. Two different types of commercial AFM cantilevers, which differed by physical dimensions and surface finish, were coated with a 10 nm film of gold and were tested in streams of argon containing mercury. The argon flow rates ranged from 5.7 to 57.4 ml/min, carrying mercury vapors at concentrations between 37 and 700 microg/m3. The results show that smaller cantilevers (approximately 140 microm x 40 microm x 4 microm) with a resonant frequency of 270-275 kHz were sensitive to less than 10 picograms of mercury, whereas larger cantilevers (approximately 245 microm x 50 microm x 7 microm) with a resonant frequency of 155-165 kHz have a sensitivity about 10 times lower. The results indicate that the kinetics of mercury capture by the gold coating follows a simple power law-correlation with the mass change (delta m) being proportional to t(n), where t is the capture time and n depends strongly on the concentration of mercury in the gas. It is also demonstrated that the mercury can be stripped off the gold coating by heating to 350 degrees C, which would allowthe cantilevers to be regenerated and reused.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".