Copper and liquid crystal polymer bonding towards lead sensing
Bibliographic record
Abstract
Abstract Lead (Pb) is a highly toxic and carcinogenic heavy metal causing adverse impacts on environment and human health, thus requiring its careful monitoring. In this work, we demonstrate the integration of copper (Cu) film-based electrodes toward Pb sensing. For this, we developed a direct bonding method for Cu thin film and liquid crystal polymer (LCP) substrate using oxygen plasma treatment followed by contact and heat at 230 °C. The oxygen plasma activation forms hydroxyl groups (OH − ) on Cu and LCP. The activated surfaces further adsorb water molecules when exposed to clean room air during contact. After contact, hydrogen bonds are formed between the OH − groups. The interfacial water is removed when the contacted films are heated, leading to shrinkage of OH − chain. This results in an intermediate oxide layer linking the Cu and C sites of Cu and LCP respectively. A strong adhesion (670 N·m −1 ) is obtained between Cu/LCP that may offer prolonged use of the electrode without delamination in wet sensing applications. Anodic stripping voltammetry of Pb using Cu thin film electrode shows a stronger current peak than sputtered Cu electrode, which implies the significance of the direct bonding approach to integrate thin films. We also studied the electrochemical impedance that will enable modeling of integrated environmental sensors for on-site monitoring of heavy metals.
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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 | 0.000 | 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".