Highly sensitive real-time microwave sensor for detection of organic liquid solvents in an oleic phase
Bibliographic record
Abstract
Accurately determining the concentration of organic solvents in an oleic phase is essential for various industrial applications, including enhanced oil recovery . Popular detection methods, like chromatographic and distillation-based approaches, suffer from sample processing-induced solvent loss. There is a lack of standard methods for detecting solvents in produced fluid streams during solvent-aided oil recovery. We propose a novel sensing approach for solvent monitoring using planar microwave sensors . The proposed sensor consists of a chipless tag-reader pair communicating wirelessly using electromagnetic coupling . The sensor has a high sensitivity response to variations in permittivity at various solvent concentrations, which is reflected in the resonance-frequency spectrum. To maximize repeatability response of sensor, the sensor is integrated into a plastic container to form a sensing probe that can be used as an on-site in-line instrument. The experiments were conducted using four solvents, including n-pentane, n-hexane, n-heptane, and ethyl acetate. The results demonstrated that when solvent concentration changes from zero to 20 wt%, the frequency shift of resonance peak changes by 2.71, 2.01, 1.66, and 2.10 MHz for the examined solvents, respectively, indicating an exceptional capability of real-time monitoring for measuring solvents in oleic phase. The proposed approach offers the potential for applying planar microwave sensors to detect organic solvents in industrial processes.
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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 | 0.000 |
| 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 | 0.001 |
| 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".