Tetrazolium Blue-Based Colorimetric Sensor for Hydrogen Gas
Bibliographic record
Abstract
Hydrogen gas (H 2(g) ) is a viable alternative to current fossil fuels due to its clean combustion, yielding water and energy. Although H 2(g) offers significant advantages as a renewable energy source, several factors impede its large-scale adoption: It is highly flammable and explosive and lacks odor. Therefore, if it is to be used as an alternative fuel source, then reliable and selective detection methods must be studied. The development of H 2(g) sensors is an ever-growing field, though current technologies are not without practical use and sensitivity limitations. In this work, we study a combination of the commercially available dye, nitrotetrazolium blue chloride (NTB), and a hydrogen-activating compound [{Ir(Cp*)(Cl)} 2 (thbpym)](Cl) 2 ] (Cp* = C 5 Me 5 –, thbpym = 4,4′,6,6′-tetrahydroxy-2,2′-bipyrimidine, IrCp*) as components of a H 2(g) detection gasochromic tape. When exposed to H 2(g), this mixture changes from pale yellow to deep blue/purple in either solution (enclosed in polydimethylsiloxane (PDMS)) or solid state (on paper), showing response times on the order of seconds. On exposure to varying H 2(g) concentrations, the sensor was additionally found to detect as little as 100 ppm of H 2(g), a value 400-fold lower than the lower flammability limit of H 2(g) (4%).
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".