Surface-Mediated Hydrogen Bonding of Proteinogenic α-Amino Acids on Silicon
Bibliographic record
Abstract
Understanding the adsorption, film growth mechanisms, and hydrogen bonding interactions of biological molecules on semiconductor surfaces has attracted much recent attention because of their applications in biosensors, biocompatible materials, and biomolecule-based electronic devices. One of the most challenging questions when studying the behavior of biomolecules on a metal or semiconductor surface is "What are the driving forces and film growth mechanisms for biomolecular adsorption on these surfaces?" Despite a large volume of work on self-assembly of amino acids on single-crystal metal surfaces, semiconductor surfaces offer more direct surface-mediated interactions and processes with biomolecules. This is due to their directional surface dangling bonds that could significantly perturb hydrogen bonding arrangements. For all the proteinogenic biomolecules studied to date, our group has observed that they generally follow a "universal" three-stage growth process on Si(111)7×7 surface. This is supported by corroborating data obtained from a three-pronged approach of combining chemical-state information provided by X-ray photoelectron spectroscopy (XPS) and the site-specific local density-of-state images obtained by scanning tunneling microscopy (STM) with large-scale quantum mechanical modeling based on the density functional theory with van der Waals corrections (DFT-D2). Indeed, this three-stage growth process on the 7×7 surface has been observed for small benchmark biomolecules, including glycine (the simplest nonchiral amino acid), alanine (the simplest chiral amino acid), cysteine (the smallest amino acid with a thiol group), and glycylglycine (the smallest (di)peptide of glycine). Its universality is further validated here for the other sulfur-containing proteinogenic amino acid, methionine. We use methionine as an example of prototypical proteinogenic amino acids to illustrate this surface-mediated process. This type of growth begins with the formation of a covalent-bond driven interfacial layer (first adlayer), followed by that of a transitional layer driven by interlayer and intralayer hydrogen bonding (second adlayer), and then finally the zwitterionic multilayers (with intralayer hydrogen bonding). The important role of surface-mediated hydrogen bonding as the key for this universal three-stage growth process is demonstrated. This finding provides new insight into biomolecule-semiconductor surface interactions often found in biosensors and biomolecular electronic devices. We also establish the trends in the H-bond length among different types of the hydrogen bonding for dimolecular structures in the gas phase and on the Si(111)7×7 surface, the latter of which could be validated by their STM images. Finally, five simple rules of thumb are developed to summarize the adsorption properties of these proteinogenic biomolecules as mediated by hydrogen bonding, and they are expected to provide a helpful guide to future studies of larger biomolecules and their potential applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".