PET Imaging of Solid Tumors with a G-Quadruplex-Targeting <sup>18</sup>F-Labeled Peptide Probe
Bibliographic record
Abstract
Bimetallic surfaces have been found to greatly improve the performance of numerous chemical processes due to synergistic interactions between the metal components. To be able to tailor the adsorption of aromatic molecules of these surfaces, the synergistic interactions within the surface and their effects on adsorbates must be elucidated. In this work, we examine the energetic and electronic interaction between benzene and several model PdFe bimetallic surfaces, with low and high Pd coverage limits, using density functional theory and compare our results with the adsorption of benzene on a pure Fe (110) surface. The adsorption energy trends on these model surfaces show that the interactions between the Pd and Fe significantly decrease the strength of benzene’s adsorption on surface Pd without significantly weakening its adsorption on the adjacent surface Fe. From the electronic analyses, the decreased adsorption strength of benzene on the model PdFe surfaces is due to the shift in the Pd’s d-band center away from the Fermi level. These results show that aromatic compounds will preferentially adsorb onto any exposed Fe in a PdFe surface due to the greater availability of electronic states near the Fermi level. Therefore, under typical catalytic conditions, the strength of the adsorption of benzene can be tailored on the basis of the amount of Pd added to an Fe surface because increasing the concentration of Pd in the surface will increase the amount of interaction between the adsorbate and the modified surface Pd.
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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 | 0.000 | 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) | 0.000 | 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 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".