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Record W2308831624 · doi:10.1021/acs.accounts.5b00534

Surface-Mediated Hydrogen Bonding of Proteinogenic α-Amino Acids on Silicon

2016· review· en· W2308831624 on OpenAlexafffund
Fatemeh Rahnemaye Rahsepar, Nafiseh Moghimi, K. T. Leung

Bibliographic record

VenueAccounts of Chemical Research · 2016
Typereview
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiomoleculeDangling bondX-ray photoelectron spectroscopyvan der Waals forceHydrogen bondAmino acidChemistryMoleculeNanotechnologyAdsorptionScanning tunneling microscopeSiliconDensity functional theorySemiconductorChemical physicsMaterials scienceComputational chemistryChemical engineeringPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.635
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.352
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreReview

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".

Quick stats

Citations37
Published2016
Admission routes2
Has abstractyes

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