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Record W7030527710

Nghiên cứu xử lý ion Pb(II) trong nước thải bằng bùn đỏ Tây Nguyên biến tính

2016· article· vi· W7030527710 on OpenAlexaboutno aff

Bibliographic record

VenueVNU Journal of Science: Natural Sciences and Technology (Vietnam National University) · 2016
Typearticle
Languagevi
FieldChemical Engineering
TopicChemical and Physical Properties in Aqueous Solutions
Canadian institutionsnot available
Fundersnot available
KeywordsSoutheast asiaNova scotiaSouth asia
DOInot available

Abstract

fetched live from OpenAlex

Tóm tắt: Bùn đỏ Tân Rai (Tây Nguyên) có độ kiềm cao ( pH = 11,2) và có hàm lượng lớn sắt oxit, nhôm oxit, silic oxit…, trong số đó một số oxit kim loại có khả năng hấp phụ kim loại nặng trong nước. Trong nghiên cứu này, chúng tôi lấy bùn đỏ Tân Rai đem rửa nước đến pH = 7 sau đó tiến hành nung ở những thời gian và nhiệt độ khác nhau từ 150oC đến 850oC. Kết quả khảo sát cho thấy vật liệu nung ở nhiệt độ 250oC trong 1 h có khả năng hấp phụ tốt đối với Pb(II) ở pH bằng 5, thời gian cân bằng hấp phụ là 180 phút, hiệu suất hấp phụ đạt 98,95%. Vật liệu hấp phụ này đã được ứng dụng thử nghiệm xử lý ion Pb(II) trong nước thải. Nước sau xử lý đạt tiêu chuẩn QCVN 24: 2009/BTNMT về chì. Từ khóa: Bùn đỏ Tây nguyên, hấp phụ chì, nước thải.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.093
Threshold uncertainty score0.311

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0930.026

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.

Opus teacher head0.010
GPT teacher head0.219
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

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