Some applications of analytical electron microscopy and high‐resolution spectroscopy in the study of functional materials
Bibliographic record
Abstract
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.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".