A Novel Technique for Determining the Adsorption Capacity and Breakthrough Time of Adsorbents Using a Noncontact High-Resolution Microwave Resonator Sensor
Bibliographic record
Abstract
A newly developed noncontact high-resolution real-time microwave sensor was used to determine the breakthrough time and adsorption capacity of adsorbents/adsorbates with different dielectric properties. The sensor is a microwave microstrip planar resonator with an enhanced quality factor using a regenerative feedback loop operating at 1.4 GHz and an adjustable quality factor of 200–200000. Beaded activated carbon (BAC, microwave-absorbing) and a polymeric adsorbent (V503, microwave transparent) were completely loaded with 1,2,4-trimethylbenzene (nonpolar) or 2-butoxyethanol (polar). During adsorption, variations in the dielectric properties of the adsorbents were monitored using two microwave parameters; quality factor and resonant frequency. Those parameters were related to adsorption breakthrough time and capacity. Adsorption tests were completed at select relative pressures (0.03, 0.1, 0.2, 0.4, and 0.6) of adsorbates in the influent stream. For all experiments, the difference between the breakthrough time ( t 5% ) and the settling time of the quality factor variation (time that the quality factor was 0.95 of its final value) was <5%. Additionally, a linear relationship between the final value of the resonant frequency shift and adsorption capacity was observed. The proposed noncontact sensor can be used to determine the breakthrough time and adsorption capacity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".