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Single cell tracking on polymer microarray reveals the impact of surface chemistry on Pseudomonas aeruginosa twitching speed and biofilm development
(The University of Nottingham, 2020-09-01)
Dataset contains raw and processed data used for the creation of figures in publication entitled 'Single cell tracking on polymer microarray reveals the impact of surface chemistry on Pseudomonas aeruginosa twitching speed ...
Sulfated glycopolymers
(University of Nottingham, 2020-04-28)
This deposit contains the raw data relating to the paper described in the title.
Contains data regarding: 1H and 13C NMR, Image analysis, in vitro and in vivo results.
Immune-instructive polymers
(The University of Nottingham, 2020-05-01)
We identify polymers which instruct different immunological responses by modulating macrophage attachment and polarisation to pro-inflammatory (M1-like) or anti-inflammatory (M2-like) phenotypes. These immune-instructive ...
Fungal biofilm formation on potential anti-attachment materials
(The University of Nottingham, 2020-06-02)
(Meth)acrylate polymers showing the lowest fungal attachment (from a preceding microarray-spot screen) were assayed by scale-up to coat the 6.4-mm diameter wells of 96-well plates. Polymers showing surface cracking were ...
Molecular quantum ring formed from a pi-conjugated macrocycle
(The University of Nottingham, 2020-10-14)
The electronic structure of a molecular quantum ring (stacks of 40-unit cyclic porphyrin polymers) is characterised via scanning tunnelling microscopy and scanning tunnelling spectroscopy. Our measurements access the ...
A linear, binary classifier to predict bacterial biofilm formation on polyacrylates
(The University of Nottingham, 2022-09-08)
Datafiles, in Microsoft Excel format, associated with the manuscript "A linear, binary classifier to predict bacterial biofilm formation on polyacrylates"
These files contain ToF-SIMS ion peaks and the bacterial attachment ...
A linear, binary classifier to predict bacterial biofilm formation on polyacrylates
(The University of Nottingham, 2022-12-21)
Research data and Python code to undertake machine learning to predict bacterial attachment to polyacrylates.