Defocused SORS imaging
Non-invasive, depth-sensitive Raman imaging for subsurface drug delivery systems and formulation monitoring.
Research profile
We work at the interface of pharmaceutical sciences, optical spectroscopy and data-driven analysis.
Our research aims to establish spectroscopy-based and data-driven approaches that provide direct, mechanistic and predictive insight into pharmaceutical materials. The group is increasingly focused on complex and macromolecular systems, including lipid-based and polymer-based materials, with emerging interest in peptide- and protein-related questions. The group is affiliated with the Center for Pharmaceutical Data Science Education, reflecting the integration of pharmaceutical sciences, instrumentation and data-driven analysis. The focus is on linking structural organization, lattice dynamics, molecular mobility and mechanical behaviour to performance-relevant properties.
Methodological directions and linked publications
3D Raman imaging
Three-dimensional and spatially resolved Raman imaging approaches for visualizing chemical and solid-state heterogeneity in pharmaceutical materials.
European Journal of Pharmaceutics and Biopharmaceutics
Microchemical Journal
SORS / SOLFRS
Spatially offset (low-frequency) Raman implementation for subsurface analysis of solid dosage forms, packaged pharmaceuticals and layered materials.
SERS / SELFRS
Sensitivity enhancement and low-frequency structural screening using nanostructured SERS substrates.
LFR-SAXS
Coupled optical spectroscopy and X-ray scattering to relate low-frequency spectral information to nanoscale structural changes.
Analytical Chemistry
Journal of Colloid and Interface Science
Molecular Pharmaceutics
Dynamics and formulation behaviour
Time-resolved and condition-dependent measurements for following structural change, molecular mobility and formulation behaviour in complex pharmaceutical systems.
Molecular Pharmaceutics
Journal of Physical Chemistry B
Crystal Growth & Design
Combined vibrational spectroscopy platform
Brillouin + low-frequency Raman + Raman
A central methodological direction is the integration of Brillouin spectroscopy, ultra-low-frequency Raman spectroscopy and conventional Raman analysis into a combined platform. The aim is to extract mechanical, structural and chemical information from the same pharmaceutical material system rather than treating these measurements as separate assays.
Why this matters
For complex formulations and heterogeneous macromolecular materials, mechanical contrast, collective structural signatures and molecular composition are often coupled. Measuring these domains together can support richer interpretation of material behaviour, formulation performance and process-relevant changes.
Computational spectroscopy and modelling
Periodic DFT with CRYSTAL
For crystalline pharmaceutical materials, we primarily use periodic density functional theory calculations with the CRYSTAL package. This supports assignment and interpretation of low-frequency Raman features, lattice dynamics, molecular packing and solid-state phase behaviour.
From spectra to mechanism
Computational models are used as interpretive tools rather than isolated calculations. The aim is to connect experimental spectra with molecular motion, structural organization and formulation-relevant material behaviour.
Integrated and multimodal experiments
Combined setups at large-scale facilities
We also work with integrated experimental environments, including combined setups at MAX IV, where optical spectroscopy can be connected with complementary structural characterization to study pharmaceutical materials across length scales.
Why combine methods?
Low-frequency Raman, Brillouin, scattering and thermal methods each probe different aspects of structure and dynamics. Combining them helps distinguish molecular packing, nanoscale organization, mechanical response and formulation-relevant phase behaviour.
Application areas
Pharmaceutical solidsPolymorphism, pharmaceutical materials, solid dosage forms, phase transitions and non-invasive structural analysis.
Drug delivery systemsLipid nanoparticles, dispersed lipid phases, polymeric carriers and formulation systems where structural organisation affects performance.
Predictive formulation toolsMechanistic spectral descriptors, multivariate analysis and machine-learning models for formulation development and decision support.