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Record W4383998797 · doi:10.1021/acs.accounts.3c00207

SERS as a Probe of Surface Chemistry Enabled by Surface-Accessible Plasmonic Nanomaterials

2023· article· en· W4383998797 on OpenAlexfundno aff
Yikai Xu, Yingrui Zhang, Chunchun Li, Ziwei Ye, Steven E. J. Bell

Bibliographic record

VenueAccounts of Chemical Research · 2023
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsnot available
FundersQueen's UniversityEast China University of Science and TechnologyQueen's University BelfastLeverhulme Trust
KeywordsNanomaterialsNanotechnologyPlasmonRaman spectroscopyNanoparticleMoleculeMaterials scienceSurface modificationSurface-enhanced Raman spectroscopyPlasmonic nanoparticlesCharacterization (materials science)ChemistryRaman scatteringOptoelectronicsPhysicsPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Conspectus When the size of materials is reduced, their volume decreases much faster than their surface area, which in the most extreme case leads to 2D nanomaterials which are “all surface”. Since atoms at the surface have free energies, electronic states, and mobility which are very different from bulk atoms, nanomaterials that have large surface-to-volume ratios can display remarkable new properties compared to their bulk counterparts. More generally, the surface is where nanomaterials interact with their environment, which in turn places surface chemistry at the heart of catalysis, nanotechnology, and sensing applications. Understanding and utilizing nanosurfaces are not possible without appropriate spectroscopic and microscopic characterization techniques. An emerging technique in this area is surface-enhanced Raman spectroscopy (SERS), which utilizes the interaction between plasmonic nanoparticles and light to enhance the Raman signals of molecules near the nanoparticles’ surfaces. SERS has the great advantage that it can provide detailed in situ information on surface orientation and binding between molecules and the nanosurface. A long-standing dilemma that has limited the applications of SERS in surface chemistry studies is the choice between surface-accessibility and plasmonic activity. More specifically, the synthesis of metal nanomaterials with strong plasmonic and SERS-enhancing properties typically involves the use of strongly adsorbing modifier molecules, but these modifiers also passivate the surface of the product material, which prevents the general application of SERS in the analysis of weaker molecule–metal interactions. In this Account, we discuss our efforts in the development of modifier-free synthetic approaches to synthesize surface-accessible, plasmonic nanomaterials for SERS. We start by discussing the definition of “modifiers” and “surface-accessibility”, especially in the context of surface chemistry studies in SERS. As a general rule of thumb, the chemical ligands on surface-accessible nanomaterials should be easily displaceable by a wide range of target molecules relevant to potential applications. We then introduce modifier-free approaches for the bottom-up synthesis of colloidal nanoparticles, which are the basic building blocks for nanotechnology. Following this, we introduce modifier-free interfacial self-assembly approaches developed by our group that allow the creation of multidimensional plasmonic nanoparticle arrays from different types of nanoparticle-building blocks. These multidimensional arrays can be further combined with different types of functional materials to form surface-accessible multifunctional hybrid plasmonic materials. Finally, we demonstrate applications for surface-accessible nanomaterials as plasmonic substrates for SERS studies of surface chemistry. Importantly, our studies revealed that the removal of modifiers led to not only significantly enhanced properties but also the observation of new surface chemistry phenomena that had been previously overlooked or misunderstood in the literature. Realizing the current limitations of modifier-based approaches provides new perspectives in manipulating molecule–metal interactions in nanotechnology and can have significant implications in the design and synthesis of the next generation of nanomaterials.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.044
GPT teacher head0.348
Teacher spread0.304 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations91
Published2023
Admission routes1
Has abstractyes

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