MétaCan
Menu
Back to cohort
Record W1603819382 · doi:10.2116/bunsekikagaku.62.785

Direct Simultaneous Multi-element Analysis in Tobacco Smoke by ICP-TOFMS after Gas Exchange

2013· article· en· W1603819382 on OpenAlexaff
Masaki Ohata, Kohei Nishiguchi, Keisuke Utani

Bibliographic record

VenueBUNSEKI KAGAKU · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsL'Alliance Boviteq
Fundersnot available
KeywordsSmokeChemistryChromatographyTobacco smokeGas analysisEnvironmental chemistryAnalytical Chemistry (journal)Organic chemistry

Abstract

fetched live from OpenAlex

空気中の浮遊粒子状物質(SPM: suspended particulate matter)を誘導結合プラズマ(ICP)に導入するため,ガス交換器(GED: gas exchange device)を試料導入系に採用した,ガス交換/誘導結合プラズマ飛行時間型質量分析法(GE/ICP-TOFMS)を開発し,たばこ煙の直接・多元素同時分析を行った.ICP-TOFMSは多元素同時分析を行うことのできる質量分析装置の一つであるので,たばこ煙の流量や煙濃度,またはたばこ煙に含まれる元素濃度が時間の経過に伴うたばこの燃焼状態の変化や環境の変化によって変動したとしても,その瞬間のたばこ煙に含まれる多元素を同時に計測することができるリアルタイム分析法として用いることができる.本GE/ICP-TOFMSによるたばこ煙の直接・多元素分析を行ったところ,たばこの先端から出る煙である副流煙からは,B,Rb,Cd,I,Tl,Pbが特徴的な元素として検出された.数種類の異なる銘柄の市販のたばこの副流煙について測定を行ったところ,元素の信号強度比を用いることで,たばこの分別が可能であることが示唆された.たばこを吸った後に呼気とともに吐出される主流煙からも副流煙と同様の元素が検出されたが,副流煙と比較したところ元素信号強度比に相違が見られた.本研究で開発したGE/ICP-TOFMSは,日本発の技術(GED)を用いた世界初の分析手法であり,たばこ煙のように時々刻々と変化するような気体試料のリアルタイム・高感度多元素同時分析法としての利用が大いに期待される.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0240.002

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 head0.008
GPT teacher head0.213
Teacher spread0.205 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations5
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueBUNSEKI KAGAKUSame topicSmart Materials for ConstructionFrench-language works237,207