Selective ion sensing with high resolution large area graphene field effect transistor arrays
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Bibliographic record
Abstract
Abstract Real-time, high resolution, simultaneous measurement of multiple ionic species is challenging with existing chromatographic, spectrophotometric and potentiometric techniques. Potentiometric ion sensors exhibit limitations in both resolution and selectivity. Herein, we develop wafer scale graphene transistor technology for overcoming these limitations. Large area graphene is an ideal material for high resolution ion sensitive field effect transistors (ISFETs), while simultaneously enabling facile fabrication as compared to conventional semiconductors. We develop the ISFETs into an array and apply Nikolskii–Eisenman analysis to account for cross-sensitivity and thereby achieve high selectivity. We experimentally demonstrate real-time, simultaneous concentration measurement of K + , Na + , $${{\rm{NH}}}_{4}^{+}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mrow><mml:mi>NH</mml:mi></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow><mml:mrow><mml:mo>+</mml:mo></mml:mrow></mml:msubsup></mml:math> , $${{\rm{NO}}}_{3}^{-}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mrow><mml:mi>NO</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo></mml:mrow></mml:msubsup></mml:math> , $${{\rm{SO}}}_{4}^{2-}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mrow><mml:mi>SO</mml:mi></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mo>−</mml:mo></mml:mrow></mml:msubsup></mml:math> , $${{\rm{HPO}}}_{4}^{2-}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mrow><mml:mi>HPO</mml:mi></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mo>−</mml:mo></mml:mrow></mml:msubsup></mml:math> and Cl − with a resolution of $$\sim\! 2\times 1{0}^{-3}\,{\mathrm{log}}\,$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>~</mml:mo><mml:mspace/><mml:mn>2</mml:mn><mml:mo>×</mml:mo><mml:mn>1</mml:mn><mml:msup><mml:mrow><mml:mn>0</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:msup><mml:mspace/><mml:mi>log</mml:mi><mml:mspace/></mml:math> concentration units. The array achieves an accuracy of ±0.05 log concentration. Finally, we demonstrate real-time ion concentration measurement in an aquarium with lemnoideae lemna over three weeks, where mineral uptake by aquatic organisms can be observed during their growth.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it