A Low-Cost, Reusable All-Solid-State TiN-Based EGFET Soil pH Sensor
Bibliographic record
Abstract
The latest farming technology utilizes specialized electrochemical soil sensors for precision agriculture. Soil pH is the primary soil attribute which influences the crop yield by affecting the availability of nutrients to plants. Hence, monitoring and maintaining optimal soil pH is of utmost importance for enhancing the farm output. In this study, a low-cost, all-solid-state electrochemical extended gate field effect transistor (EGFET) based soil pH sensor is fabricated by exploring the pH sensing and specific conductive properties of titanium nitride (TiN) in conjunction with a low-drift planar Ag/AgCl reference electrode (PARE). The thickness dependence and hydrophilicity of sputtered TiN pH sensing films on pH response is investigated. The TiN pH electrode with a thickness of 50 nm demonstrated a higher sensitivity of 57.25 mV/pH and is relatively more hydrophilic with a water contact angle (WCA) of 46.425°. The fabricated TiN-PARE soil pH sensor measured the soil pH and pHCa with a maximum relative error of 2.98% and 4.31%, when compared with a laboratory pH meter, and demonstrated a linear pH response with${R}^{{2}} \gt 0.99$. The reusability and longevity are ensured by postconditioning of the TiN pH electrode with oxalic acid. Hence, the developed TiN-PARE soil pH sensor is promising for reliable pH measurements.
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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 | 0.000 |
| Open science | 0.001 | 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".