Effect of temperature Ramp on hydrocarbon desorption profiles from zeolite ZSM‐12
Bibliographic record
Abstract
Abstract Zeolite ZSM‐12 with different Si:Al molar ratios and one‐dimensional channels with 12 oxygen ring apertures (12R) was synthesized and characterized by different techniques such as: X‐ray diffraction (XRD), scanning electron microscopy (SEM), Brunauer‐Emmett‐Teller surface area (BET), elemental analysis by atomic absorption spectroscopy, Fourier transform infrared spectroscopy (FTIR) of adsorbed pyridine, and NH3 temperature‐programmed desorption (TPD). The synthesized samples were tested as hydrocarbon (HC) trap adsorbents using toluene and ethylene as heavy and light probe molecules for HCs in the exhaust stream at engine cold‐start. High heating rate desorption experiments were performed using the Phytronix Laser Diode Thermal Desorption system (S‐960 LDTD) coupled with an Atmospheric Pressure Chemical Ionization (APCI) chamber (Phytronix Technologies, Canada) after adsorption of toluene‐ethylene mixtures. Three heating rates, which reflect the actual heating rates of the catalytic muffler, were used for the desorption process: 3 °C/s, 5 °C/s, and 9 °C/s. For all solids considered in this study, the two HCs desorbed above 240 °C, which is the light‐off temperature of the three‐way catalyst. Ag‐ZSM‐12 with different Si:Al ratios was found the most appropriate HC trapping adsorbent in spite of its slightly lower adsorption capacity. A high desorption temperature for ethylene and toluene was associated with the large density of strong Lewis acid sites in this solid.
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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 | 0.001 |
| 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 | 0.000 |
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".