The influence of H 2 partial pressure on biogenic palladium nanoparticle production assessed by single-cell ICP-mass spectrometry.
Cindy Ka Y LawEduardo Bolea-FernandezTong LiuLuiza BoninElien WallaertKim VerbekenBart De GussemeFrank VanhaeckeNico BoonPublished in: Microbial biotechnology (2022)
The production of biogenic palladium nanoparticles (bio-Pd NPs) is widely studied due to their high catalytic activity, which depends on the size of nanoparticles (NPs). Smaller NPs (here defined as <100 nm) are more efficient due to their higher surface/volume ratio. In this work, inductively coupled plasma-mass spectrometry (ICP-MS), flow cytometry (FCM) and transmission electron microscopy (TEM) were combined to obtain insight into the formation of these bio-Pd NPs. The precipitation of bio-Pd NPs was evaluated on a cell-per-cell basis using single-cell ICP-MS (SC-ICP-MS) combined with TEM images to assess how homogenously the particles were distributed over the cells. The results provided by SC-ICP-MS were consistent with those provided by "bulk" ICP-MS analysis and FCM. It was observed that heterogeneity in the distribution of palladium over an entire cell population is strongly dependent on the Pd 2+ concentration, biomass and partial H 2 pressure. The latter three parameters affected the particle size, ranging from 15.6 to 560 nm, and exerted a significant impact on the production of the bio-Pd NPs. The TEM combined with SC-ICP-MS revealed that the mass distribution for bacteria with high Pd content (144 fg Pd cell -1 ) indicated the presence of a large number of very small NPs (D50 = 15.6 nm). These results were obtained at high cell density (1 × 10 5 ± 3 × 10 4 cells μl -1 ) and H 2 partial pressure (180 ml H 2 ). In contrast, very large particles (D50 = 560 nm) were observed at low cell density (3 × 10 4 ± 10 × 10 2 cells μl -1 ) and H 2 partial pressure (10-100 ml H 2 ). The influence of the H 2 partial pressure on the nanoparticle size and the possibility of size-tuned nanoparticles are presented.
Keyphrases
- single cell
- mass spectrometry
- rna seq
- multiple sclerosis
- cell therapy
- ms ms
- high throughput
- liquid chromatography
- flow cytometry
- photodynamic therapy
- mesenchymal stem cells
- deep learning
- cell cycle arrest
- computed tomography
- stem cells
- machine learning
- oxidative stress
- high resolution
- oxide nanoparticles
- solid phase extraction