Copper selenide quantum dot (CuSeQD) materials functionalised with mercaptoalkanoic acids {3-mercaptopropionic acid (3-MPA), 6-mercaptohexanoic acid (6-MHA) and mercaptosuccinic acid (MSA)} were synthesized by a reproducible aqueous colloidal technique at room temperature. The impact of the capping agents on the size and the crystallinity of the CuSeQD materials, were investigated by small angle X-ray scattering (SAXS) and X-ray diffraction (XRD) spectroscopic techniques, respectively. SAXS results confirmed that 6-MHA-CuSeQD had the smallest average particle core size when dried, whereas MSA-CuSeQD had the smallest size in aqueous solution, though with a tendency to aggregate. Dynamic light scattering (DLS) measurements indicated strong bonding of the capping agents to CuSe particles, with MSA being the weakest binding agent, as confirmed by comparatively, low Zeta potential(ζ = À 31.1 mV) and high polydispersity index (0.469) values. UV-Vis absorption studies confirmed a large blue shift of the band gap for the QD compared to the bulk material, with characteristic absorption band (λ) and direct band gap (E gd ) values being (λ = 435 nm, E gd = 8.0 eV), (λ = 400 nm, E gd = 5.6 eV) and (λ = 340 nm, E gd = 4.0 eV), for 6-MHA-CuSeQD, 3-MPA-CuSeQD and MSA-CuSeQD, respectively. As supported by the formal potential values for 6-MHA-CuSeQD (E 0' � 120 mV), 3-MPA-CuSeQD (E 0' � 159 mV) and MSA-CuSeQD (E 0' � 183 mV), the smaller the particle size, the lower the potential required for the application of the quantum dots in an electron transfer process.[a] L.

Three-dimensional electron diffraction (3DED) techniques for structure determination has gained traction over the past few years (Gemmi et al., 2019). Rapid development such as increasing acquisition speed and automated data collection leads to large amounts of data that needs to be processed. At the same time, gained interest and implementation of 3DED as a standard practice has increased the demand for straightforward processing tools that can be used by scientist at the novice level for the specific data processing methods. To face these challenges, an extension of Scipion (de la Rosa-Trevín et al., 2016) for processing of 3DED data using DIALS has been developed under the name Scipion-ed (Bengtsson et al., 2021). In this work, the usefulness of Scipion-ed for processing a large number of 3DED datasets has been demonstrated. A total of 52 datasets were collected on as-grown tetragonal lysozyme (P 4 3 2 1 2) crystals through the continuous rotation electron diffraction method (cRED), also known as microcrystal electron diffraction (MicroED). Parallel workflows were generated in Scipion-ed for each dataset. The quality of each dataset was examined after scaling. Since the average completeness amongst all the datasets were 24 %, multiple datasets had to be merged to increase the completeness for the structure solution and refinement. Three different strategies were applied to find the appropriate datasets to merge. Strategy 1 included scaling and merging of datasets with the most favourable overall merging statistics with regard to three formulated criteria (CC(1/2) > 0.8*, I/SigI > 2 and R_meas < 0.60). 14 of the processed datasets fulfilled all criteria and were scaled and merged accordingly. Strategy 2 focused on maximizing the completeness of the final merged reflection file, without consideration of the reflection statistics of the higher resolution data. On top of the 14 previously selected datasets, additional datasets were added consecutively. Datasets that did not contribute to increased completeness were removed. Strategy 3 included merging all datasets regardless of the contribution of each individual dataset to the completeness. The different merging strategies were evaluated with respect to the ability to resolve non-modelled features in the electrostatic potential map. This was done by refining the data against a modified model lacking the Trp28 residue. A previously solved X-ray diffraction model of the tetragonal lysozyme structure (PDB: 193L, Vaney et al., 1996) was used as search model. After the refinement, the Trp28 residue added and real-space refined against the un-modelled electrostatic potential region representing the location of the residue. The final model (including Trp28) was validated against the electrostatic potential map that was refined against the modified model (without Trp28). Strategy 1 resulted in the highest correlation coefficient (CC) for the Trp28 residue (CCtrp= 0.974), along with the lowest R-value (R-work/R-free = 0.210/0.307) for the final structure model. Strategy...