Anodic Stripping Voltammetry of Arsenic(III) in the Presence of Copper(II) at a Mechanical Pencil Lead Electrode as a Screening Tool in Environmental Water
Bibliographic record
Abstract
市販のシャープペンシル芯を作用電極として用いる陽極ストリッピングボルタンメトリーによるヒ素（III）の定量法を開発した．1 M硝酸中，5 mg/L銅（II）にヒ素（III）をあらかじめ加えておき，－0.8 V vs. Ag/AgCl（3 M NaCl）における10分間の前電解により銅をシャープペンシル芯電極上に還元析出した後，リニアスイープ法により得られた酸化波は，ヒ素（III）を加えないときと比べてピーク電流値が増加し，かつピーク電位が正側にシフトした．ヒ素（III）存在時に得られた酸化波の0.15 Vより正側の電気量はヒ素（III）濃度と比例することが分かった．この電気量を用いて5～100 μg/Lの濃度範囲で相関係数0.990の検量線を得ることができ，検出下限は1.6 μg/L（3σ），50 μg/Lにおける変動係数は4.8％（n＝5）であった．試みとして，河川水及び湖水に既知量のヒ素（III）を添加したときの回収率を調べたところ，良好な結果を得た．
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".