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Data sets for "Structure of amorphous materials in the NASICON system Na_{1+x}Ti_2Si_xP_{3-x}O_{12}"
No full textData sets used to prepare Figures 3, 5 and 6 in the Journal of Physics: Condensed Matter article entitled "Structure of amorphous materials in the NASICON system Na_{1+x}Ti_2Si_xP_{3-x}O_{12}". The data sets refer to the glass structure for the compositions x = 0.8 and x = 1.0, as measured using neutron and x-ray diffraction. The diffraction results were combined with those from 29Si, 31P and 23Na solid-state nuclear magnetic resonance experiments to obtain a more complete picture of the atomic structure. NASICON is an acronym for sodium (Na) super-ionic conductor and NASICON materials are of interest as solid electrolytes and electrode materials for electrical storage energy devices. The crystalline phase can be prepared via the glass-ceramic route, leading to basic questions about the structure of the glass and how it evolves during the process of crystallisation.The data sets were collected using the methods described in the published paper.Figures 3, 5 and 6 were prepared using QtGrace (https://sourceforge.net/projects/qtgrace/). The data set corresponding to a plotted curve within an QtGrace file can be identified by clicking on that curve
Dataset for "Effects of a Web-Based, Evolutionary Mismatch-Framed Intervention Targeting Physical Activity and Diet: a Randomised Controlled Trial"
No full textThis dataset contains participant demographic, physical activity, dietary intake, health marker and cognitive determinants of behaviour data in connection with a trial of an intervention to promote physical activity and healthy eating. Both control and intervention group participants are included and data is provided for each of the three assessment points: baseline, 6 weeks (mid-trial) and 12-weeks (end of trial).The methodology can be found in the associated article
Dataset for "The effect of polymer end-group on the formation of Styrene – Maleic Acid Lipid Particles (SMALPs)"
No full textStyrene-maleic acid (SMA) copolymers have recently become the focus of attention for their ability to extract membrane proteins from cell membranes together with their native lipid environment. However, the mechanism by which these copolymers interact with lipid membranes is not well understood, nor are the key aspects of polymer structure in facilitating membrane protein extraction. The purpose for obtaining this data was to assess the effects of the copolymer end group upon solution behaviour and nanodisc formation. This dataset contains characterisation data for SMA copolymers with equivalent molecular weights and compositions (GPC, FTIR, 1H and 13C NMR spectroscopy) but varied end groups, including deuterostyrene variants used for neutron scattering experiments. End group exchange was monitored by UV-vis and 1H DOSY NMR spectroscopy. Aggregation behaviours arising from aqueous copolymer solutions, including the effects of heat treatment, were studied by dynamic light scattering (DLS) and interfacial surface tension measurements in both air and dodecane. Small angle neutron scattering (SANS) studies were used to examine the structure of copolymer aggregates, as well as that of the nanodiscs formed upon the addition of lipids, to further discern mechanistic differences.Data collection methods are described in full in the publication "The effect of polymer end-group on the formation of Styrene – Maleic Acid Lipid Particles (SMALPs)". Briefly, various copolymers between styrene and maleic anhydride were prepared by RAFT polymerisation, which, when using DDMAT, results in a relatively-large and hydrophobic SC12 end group (SMAnh-SC12). The end group was then exchanged to a CN (SMAnh-CN) through reaction with excess radical initiators. A commercial variant, SMA2000, synthesised by free-radical polymerisation was also used for comparison. All copolymers were then hydrolysed to the acid form (SMA) before workup and purification.1H and 13C NMR:
Spectra were analysed using Mestrelab MNova 11.0 software where spectra were baseline corrected and line broadening used to allow accurate integration of peak area.
GPC:
Chromatograms were analysed in Agilent GPC/SEC software to extract Mn and PDI values.
UV-vis:
The presence of the SC12 end group can be monitored by the peak at 310 nm in UV-vis spectra. To estimate the percentage end group conversion, resultant spectra were normalised by the styrenic absorbance peak at 260 nm, the concentration of which is unchanged by end group conversion. This can then be compared to the reference spectrum for a solution of SMA2000, which has no thiocarbonylthio (SC12) end groups.FTIR:
FTIR measurements were conducted on a Perkin Elmer ATR desktop spectrometer with solid-state polymer samples at room temperature.
1H & 13C NMR:
1H and 13C NMR spectra were recorded on an Agilent 500 MHz spectrometer at room temperature using d6-acetone (for anhydride species) or D2O (for acid species) as the solvent.
GPC:
GPC was conducted using an Agilent GPC 1260 Infinity chromatograph using two PLgel 5μM MIXED-D 30 cm x 7.5 mm columns with a guard column PLgel 5 μm MIXED Guard 50 x 7.5 mm. The column oven was maintained at 35 °C, with GPC-grade THF as the eluent at a flow rate of 1.00 mL/min and refractive index detection and polymer concentrations between 1.0 – 2.0 mg/mL. The system was calibrated against 12 narrow molecular weight polystyrene standards with a range of Mw from 1050 Da to 2650 kDa.
DLS:
DLS was conducted using a Malvern Zetasizer Nanoseries at theta = 173 degrees (backscattering) and wavelength = 633 nm.
Pendant Drop Tensiometry:
Tensiometry was conducted on a FTA 1000 contact angle/surface tension analyser and processed using FTA 32 surface tension image analysis software. Syringe needles were prepared by extensive washing before SMA polymers in PBS at variant concentrations were passed through these to produce a small hanging droplet which was imaged at a typical rate of 10 images per second for 10 seconds. In the case of dodecane to PBS measurements, the sample drop was suspended in a quartz cuvette of dodecane.
SANS:
SANS was performed at the ISIS Neutron and Muon Source (Rutherford Appleton Laboratory, Didcot, UK), on the Larmor and Zoom instruments (doi:10.5286/ISIS.E.RB1910182), using 1 mm quartz Hellma cells at 25 °C. Data were collected on Larmor using the standard configuration for rectangular quartz cuvettes. A wavelength band of 0.9 to 13.3 Å was used with apertures of 20x20 mm2 and 6x8 mm2 separated by a distance of 5.1 m. The sample to detector distance was 4.1 m with the detector consisting of 80, 600 mm long, position sensitive 8 mm diameter 3He tube detectors. Prior to experiments, samples were mounted in a temperature controlled multi-position sample changer.
Data were collected on the Zoom SANS instrument in the standard configuration for rectangular quartz cuvettes, with a multi-position temperature controlled sample changer. A wavelength band of 1.75 to 16.5 Å was used with apertures of 20x20 mm2 (A1) and 8x8 mm2 (A2). The source to sample distance was set to 4.0m, and the sample to detector distance was 4.0m.
Data were subsequently reduced using Mantid software and the varying solution contrasts simultaneously fit using the NIST SANS analysis package within IgorPro
Dataset for "Dense Arrays of Nanohelices: Raman Scattering from Achiral Molecules Reveals the Near-field Enhancements at Chiral Metasurfaces"
No full textThe Raman spectra of crystal violet from the SERS substrates as shown in the manuscript with 532 nm. The data is provided in the form of WiRE files and text versions for ease of access. Where applicable, before and after images from the microscope are included. Also included are extracted Raman peak heights at 1177 cm-1 (crystal violet) before and after fluorescence background removal in a spreadsheet. In addition, the original data and images for the atomic force, transmission electron microscope and scanning electron microscope images from the manuscript are included. The exported electric field distributions from simulations of the SERS substrates are also given. Each data folder contains a Metadata text file with explicit details about the nature, setup parameters and use of the data.The Raman spectra (with circularly polarised light and linearly polarised light) of crystal violet from the SERS substrates as shown in the main manuscript and supporting information with 532 nm. The data is provided in the form of WiRE files and text versions for ease of access. Where applicable, before and after images from the microscope are included. Also included are extracted Raman peak heights at 1177 cm-1 (crystal violet) before and after fluorescence background removal in a spreadsheet. In addition, the original data and images for the atomic force and scanning electron microscope images from the manuscript are included. The exported electric field distributions from simulations of the SERS substrates are also given. Each data folder contains a Metadata text file with explicit details about the nature, setup parameters and use of the data.
SERS Substrate Characterization: The substrates were characterized using scanning electron microscopy (SEM) and atomic force microscopy (AFM). The SEM micrographs in Folder: “SEM_Data” were acquired with a Jeol JSM-7900F Schottky Field Emission SEM. The AFM micrographs in Folder: “AFM_Data” were acquire using a Multimode Scanning Probe Microscope (VEECO) operating in contact mode. The TEM images in Figure 2 were acquired using a JEOL JSM-2100PLUS. For TEM, a small square (~4 mm × 3 mm) of nanohelices on Si wafer were cut and sonicated in a 0.7 mL of solvent for 20 minutes before deposition (few µL) onto TEM grids – Au nanohelices: chloroform, formvar TEM grids; Ag nanohelices: ethanol, carbon coated Cu TEM grids. The Transmission electron microscopy data can be found in “TEM_Data”.
Linearly polarised Raman Spectroscopy: Raman spectra were acquired using a Renishaw inVia Raman microscope (Folders: “532nm_Linear Pol Raman”). The incident light source for 532 nm was a continuous wave narrow bandwidth laser (Cobolt RL532-08; 50 mW). The irradiated light and epi-scattered Raman light were focused and collected through an N-plan 50× objective with a numerical aperture of 0.75. All spectra were averaged from a 40 µm × 40 µm square grid of 5×5 (25) uniformly distributed points; each separated by 10 µm. At each sample point, the spectrum acquisition was a total 10 seconds with an integration time of 1 second. The spectral resolution was 1.6 cm-1 for the spectra with 532 nm excitation. To establish the peak height relative to the baseline of the spectra in (File: “532nm_Linear Pol Raman/Peak Heights Crystal Violet_532nm.csv”), the fluorescence background was removed using Renishaw’s built-in 11th-order polynomial Intelligent Fitting™ algorithm (“subtract baseline” tool) in WiRE-version 5.3. The laser power at the sample was measured using a Thorlabs™ S175C - Microscope Slide Thermal Power Sensor (File: “532nm_Linear Pol Raman /Power_Readings_532nm_Laser.csv”). For experiments with the 532 nm continuous wave Cobolt laser, the laser power at the sample was varied between 80 µW and 25 mW using neutral density filters. The irradiance was computed by taking the measured power under the objective and dividing by the area of the spot size of the laser. The laser spot diameter was assumed to be equal to the diffraction limited size: 1.22 λ / NA; where NA = 0.75 is the numerical aperture of the objective.
Circularly polarised Raman spectroscopy: Raman optical activity data were acquired using a modified Renishaw inVia Raman microscope (see Figure 1). The incident light source for 532 nm was a polarized continuous wave narrow bandwidth laser (Cobolt RL532-08; 50 mW). A Glan-laser polarizer was used prior to the Rayleigh filter to polarize the light and a λ/2-plate (not shown in Figure 1) was placed at the output of the laser to optimize power throughput. An achromatic λ/4-plate was placed after the Rayleigh filter to circumvent the retardance properties of the Rayleigh filter. The orientation of the λ/4-plate was coarsely optimized to mitigate ellipticity at the sample using a zero-order λ/4-plate, a wire-grid polarizer to filter circularly polarized light placed above a power meter. The analyzer, was a wire-grid achromatic polarizer with an achromatic λ/2-plate in tandem to optimize for the polarization sensitivity of the spectrometer; the orientation of the λ/2-plate was optimized with a Si sample using the linearly polarized light (no λ/4-plate). With these optics in, the orientation of the λ/4-plate was fine-optimized using a piece of polycrystalline ZnSe, an N-Plan 5× (NA: 0.12) objective and a wide spectrometer slit (150 µm) to ensure parity between left-handed and right-handed circularly polarized light. The optimization data can be accessed in folders: “Circular Polarisation_CoarseOptimise”, “Circular Polarisation_FineOptimise” and “Linear Polarisation_AnalyserOptimise”. Raman optical activity experiments were performed with an N-plan 50× objective with a numerical aperture of 0.75; however, the data presented in Figure S23 was collected using the N-plan 5× objective (NA: 0.12). The circular intensity sum and difference spectra in Figure 3 to Figure 5 (folder: “ChiroptiocalRaman_42kWcm “ and “ChiroptiocalRaman_4p2kWcm”) were averaged from three pairs of 60 µm × 60 µm square grids (13×13=169) uniformly distributed points; each point separated by 5 µm and each grid separated by approximately 100 µm. At each sample point, the integration time was a total of 2 seconds for data at 42 kW cm-2 and 1 second for data at 4.2 kW cm-2. The spectral resolution was 1.6 cm-1 for the spectra with 532 nm excitation. The peak height relative to the baseline of the spectra in Figure 5 was established using the same technique as for the linearly polarized light Raman spectroscopy. For all data except that shown in Figure S22 and Figure S23, the irradiance was computed by taking the measured power under the objective and dividing by the area of the spot size of a diffraction limited spot (see Raman spectroscopy – linearly polarized light section above). The irradiance for the spectra shown in Figure S22 and Figure S23 was computed using the measured laser spot-diameter. To measure the laser spot diameter at the image plane, a Raman line scan was taken over the sharp edge of a Si sample. Then, by interpolating a cubic spline function (scripted in Python) through the Raman peak height of the OΓ-point phonon in Si (at 520 cm-1) as a function of position across the edge, the diameter could be determined from the full-width at half-maximum of the first derivative of the intensity profile. This is illustrated in Figure S22a and Figure S23a for which the accompanying data can be found in folders: “ChiroptiocalRaman_SI_3step-50xObj” and “ChiroptiocalRaman_SI_3step-5xObj”.
Simulations: Finite-difference time-domain simulations were performed in ANSYS-Lumerical™ to illustrate the electric field distributions, revealing the nature of the local field enhancements or hot-spots (Folder: “Simulations”). The simulation domain had periodic boundary conditions applied in the x and y directions to the edges of the unit cell (See Manuscript for dimensions). The domain in the vertical axis spanned -1.5 µm to 3µm depending on the size of the substrate model and had perfectly matched layer boundary conditions. The Eulerian mesh in the regions of interest was 5 Å for the nanohelices, 2.5 nm for the Au CNPs substrate and 2.5 nm for the Au G-Shaped motif nanostructures substrate. The optical properties of the Si wafer and SiO2 layer were modelled with an empirical based material model from Palik[53]. The nanohelix substrates had a 5 nm layer of SiO2; likewise 2 nm for the Au CNPs; and 100 nm for the G-Shaped Au nanostructures. The optical properties of the Au-based nanohelices were modelled using a 4:1 linear combination of the CRC[54] material models for Au and Cu. CRC based material models were also used for the optical properties of the Ag nanohelices and Au substrates. A pulsed plane wave source of light was incident on the models from 1 µm above the surface; the light was polarized parallel to the x-axis and had an amplitude of 1 V/m. For simulations with circularly polarized light, two orthogonally polarized planewave sources were superimposed with a 90° phase difference – this is contained in folder: “Simulations\Circ_Polarisation_Nanohelices”. The simulated wavelength range was 250 nm to 2.5 µm. The electric-field distributions were extracted at 532 nm and 785 nm from the cross-sectional planes indicated in the manuscript.
To simulate the AFM data in the manuscript (Folder path: “Simulations\Full_Substrate\AFM_Based_Simulations”), the AFM data for the Au G-shaped motifs and Au conglomerate nanoparticles were exported into three column text data files (.txt) using Gwyddion. These x, y, z coordinates were then reshaped into a grid in Python and imported as a surface into ANSYS™ Lumerical. The Au surface topography was then superimposed onto the relevant Si/SiO2 layered substrate model. The models in folders “G-Shaped-motif_S6”, “LeftHand_Ag_Nanohelices” and “LeftHand_AuCu_Nanohelices” of folder path: “Simulations\Full_Substrate were generated using Autodesk Inventor™ based on dimensions extracted from the SEM micrographs in folder “SEM_Data”.
The Ellipticity data were acquired through CD spectra and can be found in “CD_Spectra” folder. For each nanohelices SERS substrate, 200 spectra were acquired at rotational angles of 0° to 360° with intervals of 20°. These 200 spectra were averaged and exported into the _Average folder. The ellipticity was computed for each rotation for each sample and exported into _Ellipticity folder. These were then in tern averaged and presented in the paper. Plots of the ellipticity at every rotation and for each sample were generated and are found in the corresponding folders within the _Ellipticity folder. These plots were generated directly from the data found in the txt filed. The Python script used to analyze the data is in the subfolder “CD_Spectra_Analyzer”
Dataset for: Does design-for-deconstruction increase upfront embodied carbon? Life cycle assessment of a deconstructable building
No full textThe dataset has been prepared to summarise the life cycle inventory that has been developed to describe the environmental impacts of the building used as a case study for the study. The life cycle inventory has been prepared by completing a material take-off from the as-built drawings and material specifications for the building. The material take-off has been used to develop quantities and material specifications that can be used to represent the building without knowing the exact details of the building.The information presented in the dataset was gathered using a material take-off of the material specifications and as-built construction drawings for the case study building located in Swansea, UK. The dataset can be used to develop a model that can be used to perform a life cycle assessment to evaluate the environmental impacts of the case study building.The dataset is based on the use of ecoinvent version 3.6. The ecoinvent processes used to describe the materials in the inventory can be used to represent the life cycle inventory in other life cycle assessment databases.The LCI has been structured as described in the Readme file
Bibliographic Data from the SoTL in Civil and Structural Engineering Systematic Review
No full textThis database contains all the bibliographic information found after applying the Search Strategy used for the SoTL in Civil and Structural Engineering Systematic Review. The following electronic databases were searched: Scopus. Web of Science. OsloMet Library. Google Scholar (no bibliographic information is presented since this database does not allow to download such data). A total of 84 records were found in Scopus, 43 in Web of Science, and 55 in OsloMet Library. The search was conducted on September 1, 2023. The information is presented in .ris, .bib, and .csv format.This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 101066739
Dataset for "The 19-Item Environmental Knowledge Test (EKT-19): A Short, Psychometrically Robust Measure of Environmental Knowledge"
No full textThis dataset consists of two files – one main dataset, and one dataset for the re-test analysis. The main dataset includes an anonymous participant ID, binary responses to 30 environmental knowledge questions, participant's sex, age and duration to complete the task. The re-test dataset includes all the same variables, completed for both the initial study and at re-test after nine weeks. When collecting this dataset, we were seeking to develop a new measure of environmental knowledge, building upon an existing measure by Geiger et al. (2019).A UK sample of 346 undergraduate students completed the study via the University’s research participation scheme, 121 of whom repeated the study nine weeks later for test-retest analyses.The data is mostly binary (0,1) denoting (incorrect, correct) answers to the environmental knowledge questions. In the sex variable, 1 denotes male, and 0 female. Age is in years. Duration_EKT30 variable denotes how long the full 30 items of the environmental knowledge measure took each participant, measured in seconds (s)
Data for "Azimuthal confinement: the missing ingredient in understanding confinement loss in antiresonant, hollow-core fibres"
No full textThis dataset contains all the underlying data used in Figures 4 to 7 of the associated paper. The paper is a theoretical and computational analysis of the guidance mechanism and confinement loss in antiresonant, hollow-core optical fibres. The data includes the results of both simplified theoretical models and large scale numerical calculations for a set of cladding structures that demonstrate the effects of azimuthal confinement.All the methodology used in creating this dataset is described in the associated paper
FLEXOME software suite
No full textThis dataset primarily contains C++ source code and instructions for a set of protein analysis utilities collectively called FLEXOME. FLEXOME can perform the following functions on a protein structure input:
- Identification of covalent, hydrophobic and polar interactions using the atomic geometry of the input.
- Surface exposure and burial distance finding.
- Rigid Cluster Decomposition using pebble-game rigidity analysis.
- Normal mode finding with an elastic network model, one site per residue. Only the requested number of low-frequency modes are generated, using Cholesky decomposition and inverse iteration, to avoid the computational cost of fully inverting a large matrix.
- Geometric simulations of flexible motion in the all-atom structure, using the input atomic geometry as constraints and a normal mode eigenvector as a bias direction.
The bond-detection routine has been updated since the originally committed version 1 for improved detection of metal-ion coordination by residues, which was not fully handled in the original version due to the omission of one of the checking loops. This version should correctly detect the coordination of, for example, iron by amide and carboxyl moieties in the protein.C++ code written by Dr. Stephen A Wells, University of Bath, 2020–23.The code was written, and should be run, in a Linux command-line environment. The development environment was Cygwin on a Windows laptop with the gcc compiler. Shell scripts are written for the Bash shell. PyMOL scripts are provided to aid visualisation.Source code in the form of C++ files (.h and .cpp) is in the CPP/ directory. Useful ancillary scripts written for Bash, PyMOL and the expert systems in FLEXOME are in the SCRIPTS/ directory. The HOWTO/ directory includes a detailed user manual, "FLEXOME-GUIDANCE.txt"; a fully worked example, discussed in the manual, in the LysosymeExample/ directory; and an ExpertExample/ directory showing advanced FLEXOME usage options. Each directory includes a README.txt file summarising its contents and significance
Dataset for "Reformulating Reactivity Design for Data-Efficient Machine Learning"
No full textThis dataset contains the Gaussian 16 output files for the dataset of aza-Michael addition reactions used in the publication "Fast Identification of Reactions with Desired Barriers by Reformulating Machine Learning Activation Energies". The structures of the methylamine nucleophile, the 1000 Michael acceptor electrophiles and their 1000 transition states were all optimised at the wB97X-D/def2-TZVP level of theory with the IEFPCM(water) implicit solvent model. Before optimisation all Michael acceptors and transition states were conformationally searched using the MMFF force field in Schrödinger's MacroModel software and the lowest energy conformer was selected for DFT calculation. This dataset also contains the Gaussian 16 output files for the SVWN/def2-SVP single-point energy calculations on the dihydrogen activation catalyst and transition state structures.1000 Michael acceptor structures and their transition states for their reactions with methylamine were generated according the the scheme shown in the image "michael_structures.png" using the “R-Group Creator” and “Custom R-Group Enumeration” tools from Schrödinger's Maestro. The resulting Michael acceptors and transition states were conformationally searched using Schrödinger's MacroModel with the MMFF force field and the lowest energy electrophile and transition state conformers were selected for DFT optimisation. Gaussian 16 was used to perform geometry optimisation of the selected conformers as well as the methylamine nucleophile at the wB97X-D/def-TZVP level of theory with the IEFPCM(water) solvent model. Gaussian 16 was also used to perform single-point energy calculations on the Michael acceptor and transition state structures using the PM6 semi-empirical method with the IEFPCM(water) solvent model.
Gaussian 16 was used to perform single-point energy calculations at the SVWN/def2-SVP level of theory on all of the transition state and catalyst structures available from the "Vaska's space" dataset (https://doi.org/10.5683/SP2/CJS7QA).“R-Group Creator” and “Custom R-Group Enumeration” tools from Schrödinger Maestro v12.5. “Conformational Search” tool from Schrödinger MacroModel v12.9. Gaussian 16, Revision A.03 and Revision C.01.The "electrophiles.zip" file contains the Gaussian output files for the optimised Michael acceptor structures. The "transitionstates.zip" file contains the Gaussian output files for the optimised aza-Michael addition transition state structures. The "methylamine.out" file is the Gaussian output file for the optimised methylamine nucleophile structure. The "electrophiles_pm6.zip" file contains the Gaussian output files for the PM6 single-point energies for the Michael acceptors. The "transitionstates_pm6.zip" file contains the Gaussian output files for the PM6 single-point energies for aza-Michael addtion transition states. The "methylamine_pm6.out" file is the Gaussian output file for the PM6-optimised methylamine nucleophile structure. The "catalysts_lda.zip" file contains the Gaussian output files for the single-point LDA iridium catalyst energies. The "dihydrogen_lda.zip" file contains the Gaussian output files for the single-point LDA dihydrogen activation transition state energies. The "h2.out" file is the Gaussian output file for the LDA-optimised dihydrogen molecule
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