Nanocluster Isotope Distributions Measured by Electrospray Time-of-Flight Mass Spectrometry
Bibliographic record
Abstract
Electrospray ionization (ESI) mass spectrometry (MS) is a widely used tool for the characterization of organometallic nanoclusters. By matching experimental mass spectra with calculated isotope distributions it is possible to determine the elemental composition of these analytes. In this work we conduct ESI-MS investigations on M(14)E(13)Cl(2)(tmeda)(6) nanoclusters, where M is a transition metal, E represents a chalcogen, and tmeda is N,N,N',N'-tetramethyl-ethylenediamine. ESI mass spectra of these systems agree poorly with theoretical isotope distributions when data are acquired under standard conditions. This behavior is attributed to dead-time artifacts of the time-of-flight (TOF) analyzer used. It is well-known that excessively high TOF ion count rates lead to dead-time issues. Surprisingly, our data reveal that nanocluster spectra are affected by this problem even at moderate signal intensities that do not cause any problems for other types of analytes. This unexpected vulnerability is attributed to the extremely wide isotope distributions of the nanoclusters studied here. A good match between experimental and calculated nanocluster spectra is obtained only at ion count rates that are more than 1 order of magnitude below commonly used levels. Discrepancies between measured and theoretical isotope distributions have been observed in a number of previous ESI-MS nanocluster investigations. The dead-time issue identified here likely represents a contributing factor to the spectral distortions that were observed in those earlier studies. Using low-intensity ESI-MS conditions we demonstrate the feasibility of analyzing highly heterogeneous nanocluster samples that comprise subpopulations with a wide range of metal compositions.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".