MEMS pressure, temperature and conductivity sensors for high temperature and harsh environments
Bibliographic record
Abstract
Kraft pulp digesters have been used to convert wood chips into pulp for manufacturing a wide variety of paper products. Inside a kraft digester, chemical reactions remove lignin from their wood matrix in a caustic environment (pH~13.5, 170°C, 2MPa). Data on actual internal operating conditions in a kraft digester is needed to optimize kraft digester operation and obtain maximum production quality. Currently, this information is limited to selected static locations on the periphery of the digester. The objective of this thesis is to develop miniature temperature, pressure, and liquid conductivity sensors for use in autonomous flow-following SmartChips to measure kraft process variables within the digester during their passage through the process. Combined capacitive pressure and temperature sensors were fabricated by bonding silicon and Pyrex chips using a new polymeric gap-controlling layer and a high temperature adhesive. A simple chip bonding technique involving insertion of the adhesive into the gap between two chips was developed. A silicon dioxide layer and a thin layer of Parylene were deposited to passivate the pressure sensor diaphragm against the caustic environment in kraft digesters. The sensors were characterized at both high temperatures and pressures and no signs of corrosion could be identified on the sensors. Integrated piezoresistive pressure and temperature sensors consisting of a square silicon diaphragm and high resistance piezoresistors were developed. A new Parylene and silicone conformal coating process were developed to passivate the pressure sensors against the caustic environment. The sensors were characterized up to 2MPa and 180°C in an environmental chamber. The sensors’ resistances were measured before and after testing in a kraft pulping cycle and showed no change in their values. SEM pictures and topographical surface analyses were also performed before and after pulp liquor exposure and showed no observable changes. Combined liquid conductivity and temperature sensor packages consisting of a platinum resistance temperature detector (RTD) and a four-electrode conductivity sensor formed by stainless steel electrodes and installed on a polyetheretherketone (PEEK) enclosure were developed. The sensors were characterized up to 180°C at NaOH concentrations of 10-100g/l in the presence of wood chips and survived with no signs of corrosion.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".