In Situ Scanning Electron Microscopy Observation of Metal Nanoparticles during Heating
Bibliographic record
Abstract
Metal nanoparticles offer promising applications in facet-selective catalysis and surface plasmon resonance (SPR) [1-3]. Among these nanoparticles, silver nanoparticles have shown great potential due to their excellent optoelectronic properties and the ability to control their shape. However, the chemical stability of silver nanoparticles raises concerns. One approach to improve their chemical stability is to coat the nanoparticles with gold [1, 4]. However, the stability of these gold-coated nanoparticles and their microstructural behavior including facet preservation during heating still needs to be studied for high temperature applications. To monitor the effects of increasing temperature, in situ scanning electron microscopy (SEM) is an effective technique. In situ SEM allows for the observation of morphological change in the nanoparticles during heating, which provides insights into how their synthesis can be altered to enhance their properties. In this work, a Hitachi SU7000 SEM was used to investigate the microstructural evolution of nanoparticles during heating. The SEM heating holder was developed by Hitachi, and the heating chip was manufactured and optimized by Norcada Inc. (Edmonton Canada). For the initial experiments, gold-plated pentagonal silver nanorods were used. These nanoparticles were dropcast onto the heating chip, and images of various particles were captured at elevated temperatures during the heating process. Figure 1 presents the heating profile of the nanoparticles including the specific temperatures at which images were captured. Figure 2 shows SEM images of a cluster of four particles obtained with the upper detector (UD). Images were recorded at an accelerating voltage of 8 kV to minimize the effect of beam damage, prevent charging, and capture the surface structures. At room temperature (Figures 2A and 3A), the facets of the nano particles are clearly visible. As the temperature reaches 200 °C (Figure 2B), these facets begin to fade, and with further heating, the particles start to consolidate. At around 600 °C (Figures 2C and 3B), new lines appear on the particles, possibly cracks in the gold shell, which could lead to the sublimation of the silver inside. This behavior is observed around 600-700 °C, which coincides with the volatilization temperature of silver under low pressure, which is 680 °C [5]. Videos obtained during the heating process further illustrate the evolution of these nanoparticles. Figure 3 shows the behavior of two nanoparticles adjacent to each other during heating. At room temperature (Figure 3A), the facets are clearly visible. As the temperature increases, the facets gradually disappear and by 600 °C (Figure 3B), the nanoparticles begin to consolidate and merge together. At higher temperatures (Figure 3C), when only a shell of the nanoparticles remains, the merging of the particles becomes more apparent. The in situ SEM heating set-up used in this study proves to be an excellent method for investigating these nanoparticles. Further research will focus on gold-plated silver nanoparticles with silicate shells and nanoparticles of different geometries to determine the optimal strategies for further stabilizing silver nanoparticles at high temperature. Additionally, the study will monitor the morphological changes at different heating rates and profiles [6]. Heating profile of the nanoparticles from room temperature (RT) until 1000 °C. The red dots show the temperatures at which the SEM images in Figure 2 were captured at. SEM images of a cluster of nanoparticles at temperatures marked with red dots in Figure 1. Scale bar 100 nm. SEM images of two nanoparticles (A) coalesced and merged (B), and sublimed (C) during heating. Scale bar 100 nm.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".