Dissecting complex nanoparticle heterostructures via multimodal data fusion with aberration-corrected STEM spectroscopy

Thersleff, Thomas; Budnyk, Serhiy; Drangai, Larissa; Slabon, Adam

doi:10.1016/j.ultramic.2020.113116

Cited by 19 publications

(33 citation statements)

References 66 publications

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Section: Resultsmentioning

confidence: 99%

“…The data for these composite images were extracted from an unsupervised empirical model derived from the combination of three simultaneously acquired hyperspectral datacubes containing low-loss EELS, core-loss EELS, and EDX. These three data sets were combined using a technique known as data fusion prior to variance consolidation via orthogonalization of the joint covariance matrix, as described by Thersleff et al 36,37 More details on the analysis and the compositional maps (Figure S5) without treatment are provided in the Supporting Information. This was further verified by TEM images for all samples (Figure S1a), where the amorphous nanostructured SiO 2 dielectric spacer was observed that separated the spherical silver nanoparticles within each aggregate and prevented them from coalescing during their flame synthesis.…”

Section: ■ Introductionmentioning

confidence: 99%

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Plasmonic Coupling in Silver Nanoparticle Aggregates and Their Polymer Composite Films for Near-Infrared Photothermal Biofilm Eradication

Merkl

Zhou

Zaganiaris

et al. 2021

ACS Appl. Nano Mater.

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Plasmonic nanoparticles with near-IR (NIR) light absorption are highly attractive in biomedicine for minimally invasive photothermal treatments. However, these optical properties are typically exhibited by plasmonic nanostructures with complex, nonspherical geometries that may prohibit their broad commercialization and further integration into photothermal devices. Herein, we present the single-step aerosol self-assembly of plasmonic nanoaggregates that consisted of spherical silver nanoparticles with tunable extinction from visible to NIR wavelengths. This tunable extinction was achieved by the addition of SiO 2 during the flame synthesis of the nanoparticles, which acted as a dielectric spacer between the spherical silver nanoparticles and was also computationally validated by simulating the extinction spectra of similar silver nanoaggregates. These plasmonic nanoaggregates were easily deposited on silicone polymeric surfaces and further encased with a top polymer layer, forming plasmonic photothermal nanocomposite films. The photothermal properties of the NIR nanocomposite films were utilized to eradicate the established biofilms of clinically relevant Escherichia coli and Staphylococcus aureus, with a relationship observed between the final surface temperature and biofilm eradication.

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Section: Resultsmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

Plasmonic Coupling in Silver Nanoparticle Aggregates and Their Polymer Composite Films for Near-Infrared Photothermal Biofilm Eradication

Merkl

Zhou

Zaganiaris

et al. 2021

ACS Appl. Nano Mater.

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“…In this case, it is worth noting that organizing and conducting in-situ measurements is exceptionally difficult, either for ethical motivations (e.g., collecting biomedical samples from clinical trials) or for practical reasons (e.g. labeling environmental effects on large land and sea areas) [4], [5], [7]. Thus, when scarce training sets (either in terms of quantity or quality of the samples) are available, the analysis can be affected by overfitting on one hand, or low accuracy on the other [3], [11].…”

Section: Motivation and Main Contributions Of This Workmentioning

confidence: 99%

Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction

Marinoni,

Chlaily,

Khachatrian

et al. 2021

Preprint

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Modern data analytics take advantage of ensemble learning and transfer learning approaches to tackle some of the most relevant issues in data analysis, such as lack of labeled data to use to train the analysis models, sparsity of the information, and unbalanced distributions of the records. Nonetheless, when applied to multimodal datasets (i.e., datasets acquired by means of multiple sensing techniques or strategies), the state-of-theart methods for ensemble learning and transfer learning might show some limitations. In fact, in multimodal data analysis, not all observations would show the same level of reliability or information quality, nor an homogeneous distribution of errors and uncertainties. This condition might undermine the classic assumptions ensemble learning and transfer learning methods rely on. In this work, we propose an adaptive approach for dimensionality reduction to overcome this issue. By means of a graph theory-based approach, the most relevant features across variable size subsets of the considered datasets are identified. This information is then used to set-up ensemble learning and transfer learning architectures. We test our approach on multimodal datasets acquired in diverse research fields (remote sensing, brain-computer interfaces, photovoltaic energy). Experimental results show the validity and the robustness of our approach, able to outperform state-of-the-art techniques.

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“…The EELS and EDX data were treated following the data-fusion procedures outlined in our recent work as well as the references. 41 ■ RESULTS AND DISCUSSION XPS Analysis. A low energy-resolution survey spectrum of the petrol engine sample revealed the presence of the following elements: carbon, fluorine, oxygen, nitrogen, calcium, phosphorus, sulfur, and zinc (Figure 2a).…”

Section: ■ Experimental Sectionmentioning

confidence: 99%

“…A schematic of this approach is presented in Figure 4 with further details provided in our recent work. 41 A core-loss EELS and coregistered EDX datablock are first fused along their energy dimensions using weighted concatenation, where the weight of the EDX datablock artificially reduces its total spectral variance to be below that of the core-loss EELS. The fused datablock is then decomposed using principle component analysis (PCA).…”

Section: ■ Experimental Sectionmentioning

confidence: 99%

Soot Nanoparticles Generated from Tribofilm Decomposition under Real Engine Conditions for Identifying Lubricant Hazards

Thersleff

Jenei

Budnyk

et al. 2020

ACS Appl. Nano Mater.

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Lubrication of an internal combustion engine is critical for energy and material losses. Engine lubricants contain a number of functional additives including zinc dialkyldithiophosphate, which is a commonly used antiwear additive that forms by in situ decomposition a protective interface at the metal surface. Here, we present a detailed nanoscale investigation of carbonaceous soot nanoparticles generated from real engine conditions. By combining macroscale XPS with high-resolution STEM-EELS-EDX, we reveal that such a soot nanoparticle matrix contains also 3–5 nm ZnO-based nanoparticles with additions of phosphorus and sulfur, originating from the organometallic antiwear additive. Under the consideration of the obtained chemical information on the carbonaceous matrix and (ZnO:P,S) nanoparticles and the generally known suggestion of potential toxicity for soot nanoparticles, our method allows us to predict nanoparticle-based hazards from mechanochemical applications and also their formation mechanism. These are critical information and also the basis of toxicity assessment, both for theoretical predictions and experimental testing for the estimation of overall life-cycle analysis, including the environmental impact. Our results unravel the tribofilm decomposition under real field conditions and hint toward potentially unidentified toxicological nanoparticle hazards with respect to organophosphate-containing lubricants.

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Dissecting complex nanoparticle heterostructures via multimodal data fusion with aberration-corrected STEM spectroscopy

Cited by 19 publications

References 66 publications

Plasmonic Coupling in Silver Nanoparticle Aggregates and Their Polymer Composite Films for Near-Infrared Photothermal Biofilm Eradication

Plasmonic Coupling in Silver Nanoparticle Aggregates and Their Polymer Composite Films for Near-Infrared Photothermal Biofilm Eradication

Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction

Soot Nanoparticles Generated from Tribofilm Decomposition under Real Engine Conditions for Identifying Lubricant Hazards

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