Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
본 연구에서는 피트모스(Canadian Sphagnum peat moss)로부터 추출한 피트-휴민 (p-Humin)입자를 충전한 칼럼의 연속흐름 조건하에서의 중금속 이온(Cd, Cu, Pb)의 흡착 및 탈착효율을 조사하였다. p-Humin 충전 칼럼은 단일 성분 및 혼합 중금속 용액 모두에서 높은 중금속 제거효율을 보였으며, 실험 결과는 Thomas 모델식을 적용하여 p-Humin의 중금속 흡착특성에 대한 기초 자료를 산출하였다. 단일 성분 중금속 용액을 대상으로 한 실험 결과, p-Humin 단위 그램당 Cd, Cu 및 Pb의 최대 흡착량( $q_0$ )은 각각 138.8, 44.66 및 41.61 mg/g으로 나타났다. 혼합 중금속 용액을 대상으로 각 중금속 이온의 경쟁흡착 반응실험 결과, p-Humin에 대한 중금속 이온의 친화력 세기는 Pb $\gg$ Cu > Cd이었다. 흡착된 금속이온은 0.05 N $HNO_3$ 용액을 사용하여 쉽게 탈착시켜 회수할 수 있었으며, 회수율은 약 95% 이상을 나타냈다. 본 연구를 통해 p-Humin은 친환경적이고 경제적인 생흡착제로서 폐수 중 중금속 이온의 제거에 활용 가능함을 확인하였다. 【Peat humin(p-Humin) extracted from Canadian Sphagnum peat moss was packed in a column and removal of heavy metal ions such as Cd, Cu and Pb from aqueous solution under flow conditions was studied. The metal ions were removed not only from single-element solutions but also from a multi-metal solution. Column kinetics for metal removal were described by the Thomas model. For single-component metal solutions, the maximum adsorption capacities of the p-Humin for Pb, Cu and Cd were 138.8, 44.66 and 41.61 mg/g, respectively. The results of multi-component competitive adsorption showed that adsorption affinity was in the order of Pb $\gg$ Cu > Cd. The adsorbed metal ions were easily deserted from the p-Humin with 0.05 N $HNO_3$ solution. It is apparent that 95% of the heavy metal ions were recovered from the saturated column. This investigation provides possibility to clean up heavy-metal contaminated waste waters by using the natural biomass, p-Humin as an environmentally friendly and cost-effective new biosorbents.】
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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 it