New Developments and Applications of Solvent-Free Sampling and Sample Preparation Technologies for the Investigation of Living Systems
Bibliographic record
Abstract
In recent years there has been considerable interest in developing techniques to monitor levels of biologically active compounds in living systems in natural environments. These efforts represent a significant departure from conventional ‘sampling’ techniques, where a portion of the system under study is removed from its natural environment, and the compounds of interest extracted and analyzed in a laboratory environment. An in vivo sampling approach can eliminate errors and reduce the time associated with sample transport and storage, and can therefore result in the collection of more accurate, precise, and faster analytical data. An ideal in vivo sampling technique should be portable, solvent-free, and offer integration of the sampling, sample preparation and sample analysis steps. These requirements are met by two techniques based on coated fibre and membrane technologies, presently under development in our laboratory.[1] Fibre solid-phase microextraction (fibreSPME) involves exposing a polymer-coated fused silica fibre to a sample. The analytes partition into the fibre coating until an equilibrium is reached. The fibre is then removed from the solution and the analytes are desorbed in the injector or injection loop of an analytical instrument such as a gas chromatograph (GC). The fibre is contained in a syringe-like device to facilitate handling.[2] Fibre-SPME can be used for both spot and time-averaged sampling. For spot sampling, the fibre is typically exposed to a sample matrix until the partitioning equilibrium between sample matrix and the coating material is reached. In the time-averaged technique the fibre remains in the needle during exposure of the SPME device to the sample. The fibre coating works as a trap for analytes that diffuse into the needle. In membrane extraction with a sorbent interface (MESI) a polymeric hollow fibre or a flat sheet membrane, in contact with a sample, is fitted directly into the carrier gas line of a GC equipped with a sorbent trap.[3]Analytes partition into the polymeric phase of the membrane and, after diffusion through the membrane, are carried by the gas to the sorbent trap. The concentrated analytes are periodically delivered onto the front of the column by a thermal pulse. MESI is a dynamic system, where the rate of analyte intake is dependent on both the diffusion coefficients of analytes in the membrane material and the membrane/sample matrix distribution constant. Similar to fibre-SPME, MESI can be used for both spot and time averaged monitoring. Both fibre-SPME and MESI techniques integrate sampling, sample preparation, and sample introduction to the analytical instrument, into a simple procedure. In fibreSPME mechanical movement of the fibre is necessary, as the sampling and sample introduction steps are separated in space, allowing one instrument to analyse large numbers of fibres. MESI, on the other hand, requires a dedicated, permanently attached instrument to one or several membrane/sorbent systems, eliminating the need for mechanical movement and therefore reducing the possibility of failure. MESI is thus suitable for continuous operation, allowing conversion of the analytical separation and detection instrument into a sensor-like device suitable for monitoring operations. Calibration procedures can be made very simple in both methods. For example, in air monitoring, the air/coating distribution constant can be estimated using the linear temperature-programmed retention index system (LTPRI). This allows quantification, even without identification, as long as the stationary phase used in the analytical column is identical to the fibre coating. The diffusion coefficient can be calculated by knowing the molecular weight of the target compound. To facilitate analysis of very polar analytes, a derivatization procedure can be used. For example, the validation field measurement of formaldehyde in ambient air, using both spot and time average sampling, was conducted using several techniques. Similar results were obtained for this challenging analyte using fibre-SPME and other more established procedures.[4]
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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 | 0.001 | 0.003 |
| 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".