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8.2 Surface andLlocal Spectroscopy

2001· article· en· W1999751448 on OpenAlexaff
G. Gremaud, E. Dupas, A.V. Kulik

Bibliographic record

VenueMaterials science forum · 2001
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsImpact
Fundersnot available
KeywordsMaterials scienceSpectroscopySurface (topology)Analytical Chemistry (journal)Environmental chemistryGeometryAstronomyPhysics

Abstract

fetched live from OpenAlex

Mechanical properties of solids (elasticity, anelasticity, plasticity) are generally measured onmacroscopic samples. But many phenomena in materials science ask for measurements ofmechanical properties at the surface of a material, or at the interfaces between thin layers deposited onthe surface, or with a high spatial resolution, for example in the cases of multiphased materials,composite materials, phase transitions, lattice softening in shape-memory alloys, precipitation in lightalloys, glass transition of amorphous materials, etc.In the case of multiphased materials, such as nanomaterials, composites, alloys or polymer blends,the location of the dissipative mechanism in one phase has to be done either through modeling or byseparately studying each phase, when possible without changing its behavior. The latter is onlypossible in a limited number of cases due to the interactions between the different phases within amaterial. To give an example, the global behavior of a composite is mostly driven by the stresstransfer properties between reinforcement and matrix, which are controlled by the local mechanicalproperties in the interface region, in particular by the dynamics of the structural defects in this area[1]. It is obviously impossible to prepare a sample only composed of interface regions. Therefore, amethod for locally studying the dynamics of the structural defects will thus help make importantsteps in the understanding and the improvement of such materials.Different techniques have been developed to probe the elastic and anelastic properties of surfaces,interfaces or phases of inhomogeneous materials at the micrometer and the nanometer scales. Thesetechniques are essentially based on Scanning Probe Microscopies (SPM). One of these techniques,which was first developped in the mid-1970's, is the Scanning Acoustic Microscopy (SAM), that ispresented in paragraph 9.5 and which allows one to study the materials properties at the micrometerscale.Amongst the different ways explored to study local mechanical properties of materials, severalgroups have recently used techniques based on Scanning Microscopy (SFM) [2]. For most ofthem, the focus has been placed on elasticity, using the so-called Force Modulation Mode(FMM) at low frequencies [3]. modulation mode generally uses a large amplitude (more than10 nm), low frequency (some kHz), vibration of the sample underneath the scanning forcemicroscope tip. The component of the tip motion at the excitation frequency and the tip mean positionare simultaneously recorded, giving several images of the sample surface. In particular, the in-phaseand out-of-phase components of force modulation mode at room temperatures have been interpretedin terms of stiffness (elasticity) and damping (viscoelasticity) [4,5]. However, it has beenrecently shown [6] that the contrast of force modulation mode is dominated by friction properties,and gives only little information on the elasticity. Consequently, some care has to be taken in theinterpretation of these low-frequency studies. A way to suppress this influence of friction on thecontrast is to use smaller amplitudes (some A) at higher frequencies [7]. Scanning Local-Acceleration Microscopy (SLAM) implements this idea [8]: SLAM is a modification of contact-modescanning force microscopy. Its principle is to vibrate the sample at a frequency just above theresonance of the tip-sample system. In this case, the inertia of the tip prevents it from completelyfollowing the imposed displacement, inducing non-negligible forces and giving rise to elasticdeformation of the sample. Contact stiffness is obtained from the measure of the residualdisplacement of the tip. Mapping the contact stiffness at different temperatures with SLAM [9] hasopened the way towards local mechanical spectroscopy. Some other techniques also use highfrequencies, but with different approaches to image elasticity at room temperature [7,10,11]. Presenthigh-frequency techniques are appropriate to map properties such as stiffness or adhesion at constant

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0330.015

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 head0.008
GPT teacher head0.285
Teacher spread0.277 · 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 designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2001
Admission routes1
Has abstractyes

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