Bibliographic record
Abstract
Solid Phase Microextraction (SPME) is a flexible and convenient sampling and sample preparation technique that extracts different kinds of analytes, including both volatile and non-volatile, without the use of a solvent. The technique facilitates fast, simple and automated determination of target analytes in a range of matrices. As it offers a green methodology, it is growing in popularity as an alternative tool in analytical chemistry to traditional methods. This book follows on in spirit from the editors’ previous title, Applications of Solid Phase Microextraction and will introduce the reader to breakthrough methodologies and cutting edge applications. Although it assumes a good degree of SPME knowledge, an overview of the fundamentals is given before taking the reader through an update of the field. The reader will learn the basic principles and advantages of different SPME formats including the stir bar extraction techniques, thin film SPME, Bio-SPME, and new trends in different coatings. Applications in complex media, including food analysis, drug residues and bioanalysis are covered. Bringing together leading sample preparation academics from around the world, the editor has put together an informative new book, suitable for analytical chemists and practitioners utilising SPME tools in their research.
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 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".