Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Advanced X-ray scattering characterization for structural analyses of biobased materials
RISE Research Institutes of Sweden, Bioeconomy and Health, Material and Surface Design.
2024 (English)In: Abstracts of Papers, ACS Spring 2024, New Orleans, United States, American Chemical Society , 2024Conference paper, Published paper (Refereed)
Abstract [en]

The sustainable development goals demand a material transition from fossil-based materials to their counterparts from renewable resources. Novel biobased materials derived from forests and agricultural plants serve this purpose and have demonstrated the potential in many applications, including packaging, textiles, energy storage, electronics, etc. Despite the bright future, some fundamental challenges of developing biobased materials remain in manipulating the heterogeneity of their components and the hierarchy of their structure, as well as handling the inherent complex interaction between biobased macromols. and water. To address these challenges, advanced X-ray scattering techniques, especially at synchrotron radiation facilities, provide many possibilities to investigate the structure variation of the material in real time and with high spatial information. In this presentation, we summarize some of our recent works to investigate the water-lignocellulose interaction by coupling X-ray scattering with different testing environments and decipher the distribution of different biocomponents by using micro-focused scanning X-ray scattering imaging techniques. Furthermore, we will discuss the development of the hierarchical structure of carbonaceous materials derived from biobased materials from the perspective of X-ray scattering characterization. In the end, we will land on the data anal. of a large volume of scattering data via new tentative approaches by using machine learning methods.

Place, publisher, year, edition, pages
American Chemical Society , 2024.
National Category
Chemical Engineering
Identifiers
URN: urn:nbn:se:ri:diva-76782OAI: oai:DiVA.org:ri-76782DiVA, id: diva2:1931353
Conference
267th ACS National Meeting New Orleans, LA (Hybrid) March 17-21, 2024
Available from: 2025-01-27 Created: 2025-01-27 Last updated: 2025-09-23Bibliographically approved

Open Access in DiVA

No full text in DiVA

By organisation
Material and Surface Design
Chemical Engineering

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 11 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf