MétaCan
Menu
Back to cohort

Some applications of analytical electron microscopy and high‐resolution spectroscopy in the study of functional materials

2016· other· en· W4249699427 on OpenAlexaff
Sagar Prabhudev, Samantha Stambula, Lidia Chincilla, Hanshuo Liu, Edson P. Bellido, Isobel C. Bicket, Alexandre Pofelski, Steffi Y. Woo, Matthieu Bugnet, Stefan Loeffler, David Rossouw, Christian Wiktor, Gianluigi A. Botton

Bibliographic record

VenueEuropean Microscopy Congress 2016: Proceedings · 2016
Typeother
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsMcMaster University
Fundersnot available
KeywordsElectron energy loss spectroscopyScanning transmission electron microscopyMaterials scienceNanotechnologySpectroscopyTransmission electron microscopyDark field microscopyAnnealing (glass)Chemical physicsHigh-resolution transmission electron microscopyEnergy-dispersive X-ray spectroscopyGrapheneMicroscopyScanning electron microscopeChemistryOpticsComposite materialPhysics

Abstract

fetched live from OpenAlex

Electron microscopy has always played an important role in the development of new materials and for understanding properties of complex functional materials. The recent developments in instrumentation have significantly improved the insight that such techniques can provide, particularly for nanoscale materials and for fundamental studies related to bonding and electronic structure. In the area of functional materials, namely energy storage and conversion materials, plasmonic structures, and quantum materials, detailed microscopy is needed to optimize material properties and to understand their electronic properties. Here we highlight recent examples of work related to the study of functional materials, illustrating the crucial role of imaging and spectroscopy for the characterization and understanding of these materials. Using an aberration‐corrected TEM equipped with electron energy loss spectroscopy (EELS), we have studied the mechanism of cluster formation following atomic layer deposition on graphene nanosheets. We have also shown, with electron energy loss near‐edge structures (ELNES), that it is possible to detect the presence of N dopant atoms at different atomic sites [1]. With high‐angle annular dark‐field scanning transmission electron microscopy (HAADF‐STEM) and EELS, we have studied the evolution of alloy catalysts following in‐situ and ex‐situ annealing procedures. Starting with a disordered PtFe nanoparticle, we captured the ordering transformation, showing evidence of the formation of ordered Pt and Fe rich planes, and evidence of both Pt and Fe‐rich shells over an ordered core (Figure 1) [2]. We also showed that the Pt surface segregation induces local strain and atomic displacements [2] (Figure 2) that can be further correlated to the enhanced activity of the material [3,4]. Using in‐situ heating, it has also been possible to study the alloying phenomena of AuPt nanoparticles showing evidence of full miscibility starting at 200ºC (Figure 3), well below the thermodynamically expected temperature. At high‐temperature, we have also detected the formation of unexpected ordered structures (Figure 4). Furthermore, we found that the annealing leads to mostly phase separation and monolayer surface segregation [5]. In a related catalyst system, we have been able to study the evolution of catalysts and hybrid supports, visualizing the presence of single atom dissolution of catalysts [6]. Similar approaches have been used to study the structure of LiNi x Mn y Co 1‐x‐y O 2 (known as “NMC”) and (Li rich) NMC compounds. In this work, using a combination of HAADF‐STEM and EELS, together with multiple‐linear least squares fitting, we have demonstrated the mechanisms of charge compensation, following electrochemical cycling and the presence of monolayer‐like surface changes in the valence of transition metal ions. STEM imaging and ELNES demonstrate the presence of local heterogeneities in the Li and transition metal distribution and in the local carriers distribution. The same techniques are used to probe the localization of charges in a variety of high‐temperature superconductors [7,8]. Finally, examples of plasmonic imaging of hybridization phenomena in metallic nanostructures, together with rigorous simulations of the optical response, will be shown [9]. These examples highlight the power and versatility of analytical techniques in the TEM to solve important materials science and fundamental physics problems.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.008
GPT teacher head0.255
Teacher spread0.247 · 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
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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Microscopy Congress 2016: ProceedingsSame topicElectrocatalysts for Energy ConversionFrench-language works237,207