Directed-Sorting Method for Synthesis of Bead-Based Combinatorial Libraries of Heterogeneous Catalysts
Bibliographic record
Abstract
The synthesis and analysis of inorganic material combinatorial libraries by a directed-sorting, split-pool bead method was demonstrated. Directed-sorting, split-pool, metal-loaded libraries were synthesized by adsorbing metal salts (H2PtCl6, SnCl2, CuCl2, and NiCl2) and metal standards (Pt, Cu, Ni in HCl) onto 2-mg porous gamma-alumina beads in 96- or 384-well plates. A matrix algorithm for the synthesis of bead libraries treated each bead as a member of a row or column of a given matrix. Computer simulations and manual tracking of the sorting process were used to assess library diversity. The bead compositions were analyzed by energy-dispersive X-ray spectroscopy, X-ray fluorescence spectroscopy, electron probe microanalysis, inductively coupled plasma atomic emission spectroscopy, and inductively coupled plasma mass spectroscopy. The metal-loaded beads were analyzed by laser-activated membrane introduction mass spectroscopy (LAMIMS) for catalytic activity using methylcyclohexane dehydrogenation to toluene as a probe reaction. The catalytic activity of individual beads that showed minimal (approximately 20% of that of Pt on alumina) to high conversion could be determined semiquantitatively by LAMIMS. This method, therefore, provides an alternative to screening using microreactors for reactors that employ catalysts in the form of beads. The directed-sorting method offers the potential for synthesis of focused libraries of inorganic materials through relatively simple benchtop split-pool chemistry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".