Sensitive and Selective Electrochemical Detection of Lithium Using a Low-Cost Ion-Imprinted Polymer-Based Microfluidic Sensor
Bibliographic record
Abstract
Excessive lithium (Li⁺) discharge into water systems poses environmental and health risks, necessitating accurate and selective monitoring. Most state-of-the-art approaches involve coating receptors onto electrodes, a process that is time-, cost-, and labor-intensive. This study presents a microfluidic electrochemical sensor that integrates a stand-alone, in-situ synthesized lithium-ion imprinted polymer (Li-IIP) membrane, eliminating the need for electrode surface pretreatment and receptor-layer bonding. The Methacrylic Acid (MAA) membrane-based Li-IIP sensor achieved a limit of detection (LoD) of 168 ppb, a limit of quantification (LoQ) of 185 ppb, and sensitivity of 11.6 nA/ppm, representing a 64.9% reduction in LoD and a 4.6-fold reduction in LoQ compared to the MAA-based non-imprinted polymer (NIP) sensor, and a 6.8-fold and 8.9-fold reduction in LoD and LoQ, respectively, compared to the membrane-less sensor. Specificity studies revealed 35.5% and 138.8% greater response to Li⁺ than Na⁺ and K⁺, respectively, at 20 ppm. Selectivity studies demonstrated 25-74.6% stronger responses in Li-dominant mixtures. Interference tests showed moderate to minimal suppression from competing molecules, i.e., NaCl, KCl, NaNO₃, KNO₃, and Na₂SO₄ at 10 and 20 ppm. Recovery tests in real tap waters yielded 68.5%, 74.3%, and 93.6% for 20, 30, and 40 ppm Li⁺ spikes with less than 5% relative standard deviation. The standalone membrane-based microfluidic design of this sensor has the potential to enable a cost-effective, portable, and scalable solution for real-time Li⁺ monitoring in water quality, environmental surveillance, and lithium extraction applications in the future.
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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) | 0.000 | 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 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".