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4,010 results for “Stabilization”
Stability of seasonal cycles of vegetation at global scale
<p>Using the AVHRR product provided by NOAA, the NDVI time series has been calculated, a periodogram has been made to study different factors that affect the periodicity of the vegetative cycles. In this case, the Stability of seasonal cycles of vegetation.</p>
Soil carbon stabilization along productivity gradients in interior Alaska: Summer 2003
Boreal forests in a warmer future climate are likely to exhibit altered productivity levels, tightened fire return intervals, and increased decomposition rates to varying degrees across the landscape. This research focuses on mechanisms of soil C stabilization in P. mariana systems along gradients in stand productivity. Charred material in the soil will be quantified to understand the lasting effect of fire on the stabilization of soil C. The interaction between temperature and productivity in relation to the stabilization of soil C will be investigated by monitoring climate and soil temperatures along the productivity gradients and through laboratory incubations of soil. Research questions are addressed in three main areas of inquiry: 1) how the interaction between stand production and landscape position effect the stabilization of C throughout the soil profile, 2) how the contribution of burn residues to total C accumulation varies across the landscape, and 3) the relationship between aboveground productivity and burn residues across the landscape. The overall goal is to apply an understanding of the biophysical controls on C storage in the boreal forest to the landscape level.
Tuning the Thermal Stability and Photoisomerization of Azoheteroarenes through Macrocycle Strain
<p>Azobenzene and its derivatives are one of the most-widespread molecular scaffolds in a range of modern applications, as well as in fundamental research. After photoexcitation, azo-based photoswitches revert back to the most stable isomer in a timescale ( ) that determines the range of potential applications. Attempts to bring to extreme values prompted to the development of azobenzene and azoheteroarene derivatives that either rebalance the E- and Z- isomer stabilities, or exploit unconventional thermal isomerization mechanisms. In the former case, one successful strategy has been the creation of macrocycle strain, which tends to impact the E/Z stability asymmetrically, and thus significantly modify . On the bright side, bridged derivatives have shown an improved optical switching owing to the higher quantum yields and absence of degradation. However, in most (if not all) cases, bridged derivatives display a <em>reversed</em> thermal stability (more stable Z-isomer), and smaller than the acyclic counterparts, which restricts their potential interest to applications requiring a fast forward and backwards switch. In this paper, we investigate the impact of alkyl bridges to the thermal stability of phenyl-azoheteroarenes using computational methods, and we reveal that is indeed possible to combine such improved photo-switching characteristics while preserving the <em>regular</em> thermal stability (more stable E-isomer), and increased values under the appropriate connectivity and bridge length.</p>
Dataset for "Machine Learning Stability and Bandgaps of Lead-Free Perovskites for Photovoltaics"
<p>Datasets used in the publication "Machine Learning Stability and Bandgaps of Lead-Free Perovskites for Photovoltaics" [doi:10.1002/adts.201900178].</p> <p>All structures were relaxed with the following parameters using Quantumwise QATK 2017:</p> <p>- SG15-GGA norm-conserving (Vanderbilt) pseudopotentials employed in a LCAO-approach (200 Hartree cutoff)<br> - 2x1x2-cubic-perovskite-supercells, relaxed from cubic 11.4Åx5.7Åx11.4Å-structures (forces < 0.01eV/Å)<br> - 300K Fermi-Dirac-smearing<br> - a 6x12x6 k-point grid (Monkhorst-Pack)</p> <p><br> Specifically, the included files are:</p> <p><strong>db_2.data: </strong>the actual database used for model building (json-format)<br> <strong>lead_set.data:</strong> the "external" test set used to test predictive power with out of sample compounds (json-format)<br> <strong>load_stanley_c.py:</strong> a python script to parse the .json-files to a python-dictionary including the structures (relaxed and unrelaxed) as <a href="https://gitlab.com/ase/ase">ASE</a>-atoms</p> <p>The format of the datafiles is as follows (-1 generally denote values not parsed from the raw data):<br> {<br> "<idstring>" : {<br> "trajectory" : n/a,<br> "energy" : total DFT energy in eV,<br> "rstruc" : relaxed structure, 3-tuple: (cell-vectors, scaled_positions, elements),<br> "gaps" : { "opt_gap", "ind_gap } - both direct and indirect gap,<br> "effective_mass" : n/a,<br> "iterations" : number of relaxation steps,<br> "calc" : some calculation metadata,<br> "ustruc" : unrelaxed input structure,<br> <br> }<br> }<br> Missing ids relate to structures filtered out, because the calculation didn't converge.</p> <p>Some code which works with a different representation of this data can be found at https://github.com/jstanai/Machine-Learning-Perovskite-Properties-for-Photovoltaics</p> <p> </p> <p> </p>
Centennial clonal stability of asexual Daphnia in Greenland lakes despite climate variability
<p><strong>Daphnia_microsatellite_data_Dane_etal.2020.csv: </strong></p> <p><strong>Microsatellite genotypes from three study lakes (SS4, SS1381, and SS1590) in the Kangerlussuaq area, West Greenland. </strong>Microsatellite loci were amplified in single, 12.5 µl multiplex reactions (Type-it PCR kit, Qiagen Inc, Valencia, CA, USA), using an Eppendorf Nexus Thermal Cycler with thermal cycle conditions recommended in the Type-it PCR kit manual. Ten microsatellite primers representing genome-wide loci were used for genotyping; details in (Colbourne et al. 2004; Frisch et al., 2014). Two primers (Dp90, Dp377) failed to amplify in a consistent manner and were therefore excluded from further analysis. Amplified microsatellites were genotyped on an Applied Biosystems 3730 genetic analyser. We used the microsatellite plugin for Geneious 7.0.6 (https://www.geneious.com) for peak calling and binning. Called peaks were visually inspected and manually adjusted when necessary. </p> <p><strong>SS4_sediment.core_data_Fig2_Dane_et_al2020.xlsx</strong>: </p> <p><strong>Information on various parameters of sediment cores collected in Lake SS4, Kangerlussuq area, West Greenland. </strong>Data used in Dane et al. 2020, Figure 2 (panels B and C) are derived from two sediment cores: one for fluorescence (section at 0.5 cm intervals, <em>Depth</em>) and one for <em>Daphnia </em>ephippia analyses (1-cm intervals). Percentage organic matter content (loss-on-ignition at 550 °C,<em>OM%</em>) was used to correlate the two cores to each other and to a previously-dated sediment core (see Dane et al. 2020, Methods). The fluorescence derived parameter Parafac component C2 was used as an indicator of the abundance of purple sulphur bacteria. The organic carbon burial rate (<em>OC AR</em>, g C m–2 yr–1) was also calculated for this core (see Anderson et al. 2019). The <em>Daphnia</em> core was used for the microsatellite analyses and the accumulation rate of ephippia (<em>ephippia AR</em>) at the core site was estimated.</p> <p>For further details please see associated publication in Ecology and Evolution.</p> <p> </p>
Autofluorescence-Free In Vivo Imaging Using Polymer-Stabilized Nd3+-Doped YAG Nanocrystals
<p>Neodymium-doped yttrium aluminum garnet (YAG:Nd<sup>3+</sup>) has been widely developed during roughly the last sixty years and has been an outstanding fluorescent material. It has been considered as the gold standard among multipurpose solid-state lasers. Yet, the successful downsizing of this system into the nano regimen has been elusive, so far. Indeed, the synthesis of a garnet structure at the nanoscale, with enough crystalline quality for optical applications was found to be quite challenging. Here, we present an improved solvothermal synthesis method producing YAG:Nd<sup>3+</sup> nanocrystals of remarkably good structural quality. Adequate surface functionalization using asymmetric double-hydrophilic block copolymers, constituted of a metal-binding block and a neutral water soluble block, provides stabilized YAG:Nd<sup>3+</sup> nanocrystals with a long term colloidal stability in aqueous suspensions. These newly stabilized nanoprobes keep the spectroscopic quality (long lifetimes, narrow emission lines, and large Stokes shift) characteristic of bulk YAG:Nd<sup>3+</sup>. The narrow emission lines of YAG:Nd<sup>3+</sup> nanocrystals are exploited by differential infrared fluorescence imaging, thus achieving an autofluorescence-free <em>in vivo</em> readout. In addition, nanothermometry measurements, based on the ratiometric fluorescence of the stabilized YAG:Nd<sup>3+</sup> nanocrystals, are demonstrated. The progress here reported paves the way for the implementation of this new stabilized YAG:Nd<sup>3+</sup> system in the preclinical arena.</p>
Stabilizing or Destabilizing: Simulations of Chymotrypsin Inhibitor 2 under Crowding Reveal Existence of a Crossover Temperature
<p>This data accompanies the paper entitled <em>Stabilizing or Destabilizing: Simulations of Chymotrypsin Inhibitor 2 under Crowding Reveal Existence of a Crossover Temperature</em> (https://dx.doi.org/10.1021/acs.jpclett.0c03626).</p> <p>CI2_REST2.zip: The zip archive includes REST2 trajectories for the three systems investigated in the paper: dilute conditions, crowding by BSA, and crowding by lysozyme. The trajectories are saved in the GROMACS XTC file format, separately for each temperature (i=0,...,23). Given the large trajectory sizes, only protein coordinates (CI2 + crowder(s)) are reported, and the output frequency is reduced to 100 ps. A starting geometry (in the Gromos87 GRO format) after a short relaxation is provided for each REST2 simulation (conf_prot.gro). Moreover, for each REST2 simulation, an xarray (http://xarray.pydata.org) dataset, saved in the netCDF file format, is included with the following observables computed for CI2: fraction of native contacts relative to crystal structure, radius of gyration, secondary-structure content, fraction of native contacts evaluated separately for the alpha helix and the two beta strands.</p>
Shear-stabilized jammed packings
<p>Authors are listed in alphabetical order.</p> <p>This data set contains approximately 140,000 shear-stabilized jammed packings, as described in [1].</p> <p>These packings contain <span class="math-tex">\(N = 16 \ldots 4096\)</span> particles with harmonic interactions, under a confining pressure <span class="math-tex">\(p=10^{-7}\ldots10^{-2}\)</span>. Ensemble sizes range from 10 (N=4096) to 5000 (N=16). </p> <p><strong>Particle interactions</strong></p> <p>The simulation code minimizes the enthalpy</p> <p><span class="math-tex">\(H = \sum_{} \frac{k}{2} \delta_{ij}^2 + pL^2\)</span></p> <p>where L² is the simulation box area, p the externally applied pressure, k=1 the spring constant and </p> <p><span class="math-tex">\(\delta_{ij} = \left\{ \begin{array}{ll} R_i + R_j - |\vec{r_{ij}}| & \textrm{if } |\vec{r_{ij}}| < R_i + R_j, \\ 0 & \textrm{otherwise.} \end{array}\right.\)</span></p> <p> </p> <p><strong>Data files</strong></p> <p>The packings are stored in an HDF5 data file, with the following format:</p> <ul> <li>Example name: N1024P3162e-3_tables.h5 <ul> <li>Packings with <span class="math-tex">\(N=1024\)</span> particles</li> <li>Pressure <span class="math-tex">\(p = 3.162\cdot 10^{-3}\)</span></li> </ul> </li> <li>/packing_attr_cache is a data table containing properties of each packing, such as <ul> <li>the lattice vectors L1 and L2, describing the positions of periodic copies,</li> <li>sxx, syy, sxy, the boundary stresses,</li> <li>phi, the packing fraction,</li> <li>N - Ncorrected, the effective number of particles,</li> <li>Z, the contact number, and</li> <li>path, the path in the HDF5 file this packing can be found</li> </ul> </li> <li>Packings are stored in a directory structure, e.g. /N1024/P3.1620e-03/0090 <ul> <li>Each directory has attributes with the same data as in packing_attr_cache </li> <li>Each directory contains a table 'particles' which stores x,y and r. <ul> <li>HDF5 does not support float128 values, so the positions are stored as two float64 values x and x_err. Sum them as float128 to get the full-resolution value.</li> </ul> </li> </ul> </li> </ul> <p> </p> <p>[1] Simon Dagois-Bohy, Brian P. Tighe, Johannes Simon, Silke Henkes, and Martin van Hecke. <em>Soft-Sphere Packings at Finite Pressure but Unstable to Shear. </em>Phys. Rev. Lett. <strong>109</strong>, 095703. http://dx.doi.org/10.1103/PhysRevLett.109.095703</p>
Contact changes in shear-stabilized jammed packings
<p>Authors are listed in alphabetical order.</p> <p>This data set contains the result of small simple shear deformations applied to approximately 140,000 shear-stabilized jammed packings (see [...]), focusing on contact changes, as described in [1,2,3,4].</p> <p>These packings contain <span class="math-tex">\(N = 16 \ldots 4096\)</span> particles with harmonic interactions, under a confining pressure <span class="math-tex">\(p=10^{-7}\ldots10^{-2}\)</span>. Ensemble sizes range from 10 (N=4096) to 5000 (N=16). </p> <p>In addition, a data file summarizing properties of the first contact change for each packing is provided.</p> <p><strong>Particle interactions</strong></p> <p>The simulation code minimizes the enthalpy</p> <p><span class="math-tex">\(H = \sum_{} \frac{k}{2} \delta_{ij}^2 + pL^2\)</span></p> <p>where L² is the simulation box area, p the externally applied pressure, k=1 the spring constant and </p> <p><span class="math-tex">\(\delta_{ij} = \left\{ \begin{array}{ll} R_i + R_j - |\vec{r_{ij}}| & \textrm{if } |\vec{r_{ij}}| < R_i + R_j, \\ 0 & \textrm{otherwise.} \end{array}\right.\)</span></p> <p> </p> <p>During shear, the boundary conditions are changed, and the system is relaxed to the new state. The simulation uses a bisection algorithm to efficiently step towards each subsequent contact change; see [3,4] for details.</p> <p><strong>Data files</strong></p> <p>The packings are stored in HDF5 data files. For each ensemble, we provide two files: one with and one without particle positions:</p> <ul> <li>N1024~P3162e-3_shear_noparticles.h5 includes all simulation data, but omits particle positions (see below for which data is included).</li> <li>For small data sets, N1024~P3162e-3_shear.h5 contains all simulations and all particle positions</li> <li>For large data sets, N1024~P3162e-3_shear_partial.h5 contains <em>a subset</em> of all simulations, but with all particle positions.</li> <li>Full particle positions for all simulations are available upon request to the authors. Please contact Martin van Hecke .</li> </ul> <p>All files follow the same HDF5 layout:</p> <ul> <li>Example name: N1024~P3162e-3_shear.h5 and N1024~P3162e-3_shear_noparticles.h5 <ul> <li>Packings with <span class="math-tex">\(N=1024\)</span> particles</li> <li>Pressure <span class="math-tex">\(p = 3.162\cdot 10^{-3}\)</span></li> </ul> </li> <li>Packings are stored in a directory structure, e.g. /N1024/P3.1620e-03/0090/SR for the packing with id 0090. <ul> <li>This directory contains a table 'data' indicating system parameters for each simulation step: <ul> <li>boundary conditions L1 and L2 (also as L, alpha, delta)</li> <li>pressure P,</li> <li>strain gamma,</li> <li>stresses s_xy (simple shear), s_xx and s_yy, </li> <li>number of contacts Ncontacts, contact number Z and number of rattlers #rattler</li> <li>number of changed contacts for this contact change bisection Nchanges, N+ (created), N- (broken)</li> <li>contact number Z</li> <li>energy U, enthalpy H and their change in the last relaxation step (dU, dH)</li> <li>step runtime t_run (seconds), #CG, #FIRE (number of conjugate gradient and FIRE iterations)</li> <li>path to the packing corresponding to this state (does not always exist for each state for older simulations)</li> </ul> </li> <li>Each state is saved in /N1024/P3.1620e-03/0090/SR/0000 (initial), /N1024/P3.1620e-03/0090/SR/0001, ...etc. <ul> <li>States are not included in the _noparticles.h5 files</li> <li>Some files omit intermediate positions, and only store positions just before and just after a contact change.</li> <li>The format of these directories is the same as in https://dx.doi.org/10.5281/zenodo.59216.</li> </ul> </li> </ul> </li> </ul> <p>Finally, we provide a summary file (shear_summary_cache.h5) which contains one table ('data') with properties of the first contact change of all packings. We provide the following columns:</p> <ul> <li>The variable postfix determines whether the value was calculated in the initial state (_base), just before the first contact change (_min) or just after the first contact change (_plus).</li> </ul> <p> </p> <ul> <li>General/simulation properties <ul> <li>Number of particles 'N'</li> <li>Random seed ['num', 'PackingNumber_base']</li> <li>External pressure 'P0_base'</li> <li>Simulation step ['i_min', 'i_plus']</li> </ul> </li> <li>Relaxation statistics <ul> <li>Last change in enthalpy during relaxation ['dH_base', 'dH_plus', 'dH_min']</li> <li>Last change in energy during relaxation ['dU_base', 'dU_plus', 'dU_min']</li> <li>Maximum gradient ['maxGrad_base', 'gg_min', 'gg_plus']</li> <li>Initial simulation runtime ['runtime (s)_base']</li> </ul> </li> <li>State properties <ul> <li>Number of rattlers 'N - Ncorrected_base'</li> <li>Number of non-rattler particles ['Neff_min', 'Neff_plus']</li> <li>Number of contacts ['Ncontacts_plus', 'Ncontacts_min']</li> <li>Contact number z ['Z_base', 'Z_min', 'Z_plus']</li> <li>Internal pressure ['P', 'P_base', 'P_min', 'P_plus']</li> <li>Mean overlap δ ['mean_delta_base']</li> <li>Packing fraction ['phi_base', 'phi_min', 'phi_plus']</li> <li>Enthalpy ['H_base', 'H_plus', 'H_min']</li> <li>Energy ['Uhelper_base', 'U_min', 'U_plus']</li> <li>Simple shear parameter alpha ['alpha_base', 'alpha_min', 'alpha_plus']</li> <li>Pure shear parameter delta ['delta_base', 'delta_plus', 'delta_min']</li> <li>Square root of area ['L_base', 'L_min', 'L_plus']</li> <li>Stresses on boundaries: <ul> <li>xx ['sxx_base', 's_xx_min', 's_xx_plus',]</li> <li>yy [ 'syy_base', 's_yy_plus', 's_yy_min',]</li> <li>xy ['sxy_base', 's_xy_plus', 's_xy_min']</li> </ul> </li> <li>Elastic moduli: <ul> <li> ['c1_base', 'c1_min', 'c1_plus',</li> <li>'c2_base', 'c2_min', 'c2_plus',</li> <li>'c3_base', 'c3_min', 'c3_plus',</li> <li>'c4_base', 'c4_plus', 'c4_min',</li> <li>'c5_base', 'c5_plus', 'c5_min',</li> <li>'c6_base', 'c6_min', 'c6_plus',</li> <li>'Dac_base', 'Dac_plus', 'Dac_min',</li> </ul> </li> <li>AC component of G(θ) ['Gac_base', 'Gac_min', 'Gac_plus']</li> <li>DC component of G(θ) ['Gdc_base', 'Gdc_min', 'Gdc_plus',]</li> <li>AC component of U(θ) ['Uac_base', 'Uac_plus', 'Uac_min']</li> <li>DC component of U(θ) ['Udc_base', 'Udc_plus', 'Udc_min']</li> <li>Simple shear ['Galpha_base', 'Galpha_plus', 'Galpha_min' ]</li> </ul> </li> <li>Contact change properties <ul> <li>Applied strain gamma ['gamma_plus', 'gamma_min'] <ul> <li><em>gamma_min is used as contact change strain</em></li> </ul> </li> <li>Number of created/broken contacts ['N+_plus', 'N+_min', 'N-_plus', 'N-_min']</li> <li>Number of changed contacts (=N<sup>+</sup> + N<sup>-</sup>) ['Nchanges_plus', 'Nchanges_min']</li> <li>Making & breaking strain from upar and uperp: <ul> <li>simple linear (SL) solution: ['gmk_SL_base', 'gbk_SL_base']</li> <li>full quadratic (FQ) solution: ['gmk_FQ_base' 'gbk_FQ_base']</li> </ul> </li> <li>G up to CC from fit σ=Gγ & error bar ['Glin', 'Glinerr']</li> <li>G up to CC from fit σ=Gγ + λγ² & error bar ['Gquad', 'Gquaderr']</li> <li>λ up to CC from fit σ=Gγ + λγ² & error bar ['lambdaquad', 'lambdaquaderr']</li> </ul> </li> </ul> <p>[1] Simon Dagois-Bohy, Brian P. Tighe, Johannes Simon, Silke Henkes, and Martin van Hecke. <em>Soft-Sphere Packings at Finite Pressure but Unstable to Shear. </em>Phys. Rev. Lett. <strong>109</strong>, 095703. http://dx.doi.org/10.1103/PhysRevLett.109.095703</p> <p>[2] Merlijn S. van Deen, Johannes Simon, Zorana Zeravcic, Simon Dagois-Bohy, Brian P. Tighe, and Martin van Hecke. <em>Contact changes near jamming</em>. Phys. Rev. E <strong>90</strong> 020202(R). http://dx.doi.org/10.1103/PhysRevE.90.020202</p> <p>[3] Merlijn S. van Deen, Brian P. Tighe, and Martin van Hecke. <em>Contact Changes of Sheared Systems: Scaling, Correlations, and Mechanisms</em>. arXiv:1606.04799. https://arxiv.org/abs/1606.04799</p> <p>[4] Merlijn S. van Deen. <em>Mechanical Response of Foams: Elasticity, Plasticity, and Rearrangements</em>. PhD Thesis, Leiden University, 2016. https://openaccess.leidenuniv.nl/handle/1887/40902</p>
Additive interfacial chiral interaction in multilayers for stabilization of small individual skyrmion at room temperature
<p>International audience Facing the ever-growing demand for data storage will most probably require a new paradigm. Nanoscale magnetic skyrmions are anticipated to solve this issue as they are arguably the smallest spin textures in magnetic thin films in nature. We designed cobalt-based multilayered thin films where the cobalt layer is sandwiched between two heavy metals providing additive interfacial Dzyaloshinskii-Moriya interactions, which reach a value close to 2 mJ m-2 in the case of the Ir|Co|Pt asymmetric multilayers. Using a magnetization-sensitive scanning x-ray transmission microscopy technique, we imaged small magnetic domains at very low field in these multilayers. The study of their behavior in perpendicular magnetic field allows us to conclude that they are actually magnetic skyrmions stabilized by the large Dzyaloshinskii-Moriya interaction. This discovery of stable sub-100 nm individual skyrmions at room temperature in a technologically relevant material opens the way for device applications in a near future. on</p>
Mutual Induced Fit Transition Structure Stabilization of Corannulene's Bowl-to-Bowl Inversion in a Perylene Bisimide Cyclophane
<p>Additional data to report <a href="https://doi.org/10.1039/D3SC05341E">https://doi.org/10.1039/D3SC05341E</a>:<br><br>Corannulene is known to undergo a fast bowl-to-bowl inversion at r.t. <em>via</em> a planar transition structure (TS). Herein we present the catalysis of this process within a perylene bisimide (PBI) cyclophane composed of chirally twisted, non-planar chromophores, linked by <em>para</em>-xylylene spacers. Variable temperature NMR studies reveal that the bowl-to-bowl inversion is significantly accelerated within the cyclophane template despite the structural non-complementarity between the binding site of the host and the TS of the guest. The observed acceleration corresponds to a decrease in the bowl-to-bowl inversion barrier of 11.6 kJ mol<sup>−1</sup> compared to the uncatalyzed process. Comparative binding studies for corannulene (20 π-electrons) and other planar polycyclic aromatic hydrocarbons (PAHs) with 14 to 24 π-electrons were applied to rationalize this barrier reduction. They revealed high binding constants that reach, in tetrachloromethane as a solvent, the picomolar range for the largest guest coronene. Computational models corroborate these experimental results and suggest that both TS stabilization and ground state destabilization contribute to the observed catalytic effect. Hereby, we find a “mutual induced fit” between host and guest in the TS complex, such that mutual geometric adaptation of the energetically favored planar TS and curved π-systems of the host results in an unprecedented non-planar TS of corannulene. Concomitant partial planarization of the PBI units optimizes noncovalent TS stabilization by π–π stacking interactions. This observation of a “mutual induced fit” in the TS of a host–guest complex was further validated experimentally by single crystal X-ray analysis of a host–guest complex with coronene as a qualitative transition state analogue.</p>
Stability Increase of Phenolic Acid Decarboxylase by a Combination of Protein and Solvent Engineering Unlocks Applications at Elevated Temperatures
<p>Enzymatic decarboxylation of biobased hydroxycinnamic acids gives access to phenolic styrenes for adhesive production. Phenolic acid decarboxylases are proficient enzymes that have been applied in aqueous systems, organic solvents, biphasic systems, and deep eutectic solvents, which makes stability a key feature. Stabilization of the enzyme would increase the total turnover number and thus reduce the energy consumption and waste accumulation associated with biocatalyst production. In this study, we used ancestral sequence reconstruction to generate thermostable decarboxylases. Investigation of a set of 16 ancestors resulted in the identification of a variant with an unfolding temperature of 78.1 °C and a half-life time of 45 h at 60 °C. Crystal structures were determined for three selected ancestors. Structural attributes were calculated to fit different regression models for predicting the thermal stability of variants that have not yet been experimentally explored. The models rely on hydrophobic clusters, salt bridges, hydrogen bonds, and surface properties and can identify more stable proteins out of a pool of candidates. Further stabilization was achieved by the application of mixtures of natural deep eutectic solvents and buffers. Our approach is a straightforward option for enhancing the industrial application of the decarboxylation process.</p>
Siloxide tripodal ligands as a scaffold for stabilizing lanthanides in the +IV oxidation state
<p>This upload contains raw data (NMR, X-Ray, EPR, Cyclic Voltammetry, UV, IR, Magnetism) files for the article</p>
Replication Data for Lead-Free Semiconductors, Phase-Evolution and Superior Stability of Multinary Tin Chalcohalides
<p>Tin-based semiconductors are highly desirable materials for energy applications due to their low toxicity and biocompatibility relative to analogous lead-based semiconductors. In particular, tin-based<br>chalcohalides possess optoelectronic properties that are ideal for photovoltaic and photocatalytic applications. In addition, they are believed to benefit from increased stability compared with halide perovskites.<br>However, to fully realize their potential, it is first necessary to better understand and predict the synthesis and phase evolution of these complex materials. Here, we describe a versatile solution-phase method for the<br>preparation of the multinary tin chalcohalide semiconductors Sn2SbS2I3, Sn2BiS2I3, Sn2BiSI5, and Sn2SI2. We demonstrate how certain thiocyanate precursors are selective toward the synthesis of chalcohalides, thus<br>preventing the formation of binary and other lower order impurities rather than the preferred multinary compositions. Critically, we utilized 119Sn ssNMR spectroscopy to further assess the phase purity of these materials. Further, we validate that the tin chalcohalides exhibit excellent water stability under ambient conditions, as well as remarkable resistance to heat over time compared to halide perovskites. Together, this work enables the isolation of lead-free, stable, direct band gap chalcohalide compositions that will help engineer more stable and biocompatible semiconductors and devices.</p>
Research data supporting "Observation of a Topological Edge State Stabilized by Dissipation"
<div> <p>This repository contains the data presented in the manuscript titled "Observation of a Topological Edge State Stabilized by Dissipation" by H. Wetter et al., Phys. Rev. Lett. 131, 083801 (2023). The files contain the final data sets relevant to reproduce all plots shown in the paper. Data types are CSV, TIF, SVG, TXT, PNG. No licensed software is required for opening and reading the files.</p> </div>
Data for "Unfolding the structural stability of nanoalloys via symmetry-constrained genetic algorithm and neural network potential"
<p><strong>PtNi_alloy_eam.db</strong> is the dataset (ase.db object) consisting of 55982 intially sampled Pt-Ni alloy structures with EAM energies and forces.</p> <p><strong>PtNi_alloy_dft.db</strong> is the dataset (ase.db object) consisting of the final 6828 resampled Pt-Ni alloy structures with DFT energies and forces calculated by VASP. This is the training set for the NNP, and could be very useful for fitting other machine learning models.</p> <p><strong>PtNi_nanoalloy_vertices_nnp.db</strong> is the dataset (ase.db object) consisting of all the vertices (stable structures) on the convex hulls obtained from NNP-based SCGA runs on 36 Pt-Ni nanoalloy systems. The energies are given by the NNP. Additional information such as mixing energy, motif and symmetry axis are also saved in the dataset and can be queried by the 'data' keyword. An xyz format trajectory of these stable structures is also uploaded.</p> <p>All the input files and scripts for hybrid MC-MD simulations, QBC resampling, DFT calculations, NNP training, NNP-based SCGA runs and convex hull analysis are provided in <strong>inputs_and_scripts.zip</strong>.</p>
HARVIS Non Stabilized Assistant flight simulation parameters
<p>This dataset regroups data from the test of the HARVIS Non Stabilised Approach assistant.<br> The experiment consisted in testing the assistant in realistic conditions in single pilot operations on an A320 research simulator.<br> The validation session with a participant was composed of 6 scenarios. 3 scenarios were played with the assistant support and 3 without it. <br> Pilots were seated in the left seat of the cockpit and were told to land on runway 25 of Paris Orly Airport. The Aircraft was positioned approximately at 7NM before runway threshold. <br> Initial conditions (A/C speed, position, flaps configuration, landing gear state, wind…) varied from one test to another impacting the difficulty of the approach. <br> Pilots were briefed about the meteorological situation on the approach before each test. <br> When ready, the test begun, and pilots had to manually control the A/C in Visual Meteorological Conditions with the objective to stabilize the A/C for landing. <br> The assistant provided alerting in case of diverging parameters and assisted the pilot in the go around decision-making at the stabilization gate (500ft above airport elevation).<br> The participants were told to stabilize the A/C before stabilization point. <br> At stabilization point, participants had to follow assistant’s order unless they thought the order inappropriate.</p> <p>1 file is provided for each test:</p> <p>analysis_flight_parameters_P00X_SX_(No)Harvis.csv<br> Regroups the flight parameters recorded during each test. At each timestamp, each parameter have been analysed to see if there are within limits defined for the assistant.<br> Each file is identified by a participant number "P00X", a scenario number "SX". If the participant was assisted by the assistant, the file is tagged with "Harvis", if not, the file is with "NoHarvis"</p> <p><br> </p>
A saturation-mutagenesis analysis of the interplay between stability and activation in Ras
<p>Dataset for the Hidalgo et al. eLife paper DOI: <a href="https://doi.org/10.7554/eLife.76595">https://doi.org/10.7554/eLife.76595</a></p>
Dataset: Infrared-radiofluorescence: dose saturation and long-term signal stability of a K-feldspar sample
<p>Original measurement and processed data of the study <em>Infrared-radiofluorescence: dose saturation and long-term signal stability of a K-feldspar sample </em>submitted for review to Radiation Measurements. The data are structured as follows:</p> <ol> <li><strong>Measurement data </strong></li> <li><strong>Processed data</strong></li> </ol> <p>Experiments were carried out at the Archéosciences Bordeaux (UMR 6034, CNRS - Université Bordeaux Montaigne; former IRAMAT-CRP2A) in Bordeaux (France) and at the Département des sciences de la Terre of the Université du Québec à Montréal (Canada). The subfolders are organised by the laboratory where the experiments were carried out: spectrometer measurements in Montréal (00_Montreal_Spectrometer) and spatially resolved measurements (camera) in Bordeaux (10_Bordeaux_Camera). </p> <p><strong>Measurement data </strong>contains sequence files used to run the experiments (so-called *.lseq files) as well as the raw, unaltered measurement output in the form of files with the ending *.xsyg and *.tiff. For the camera measurements in Bordeaux, the system returned a couple of single TIFF files. We merged those files in two files, one for <em>RF<sub>nat</sub></em> and <em>RF<sub>reg</sub></em>, for convenience reasons. The data are, however, unprocessed. </p> <p><strong>Processed data</strong> is organized like the measurement data folder containing all kinds of semi-automated processed data (PDF files, images). All data were processed with the R (R Core Team, 2021) package 'Luminescence' (Kreutzer et al., 2012; 2021) and an <em>ImageJ </em>macro detailed in Mittelstraß and Kreutzer (2021)</p> <p> </p> <p><strong>References</strong></p> <p>Kreutzer, S., Schmidt, C., Fuchs, M.C., Dietze, M., Fischer, M., Fuchs, M., 2012. Introducing an R package for luminescence dating analysis. Ancient TL 30, 1–8.</p> <p>Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Smedley, R.K., Christophe, C., Zink, A., Durcan, J., King, G.E., Philippe, A., Guérin, G., Riedesel, S., Autzen, M., Guibert, P., Mittelstrass, D., Gray, H.J., 2021. Luminescence: Comprehensive luminescence dating data analysis. CRAN. https://doi.org/10.5281/zenodo.4729933</p> <p>Mittelstraß, D., Kreutzer, S., 2021. Spatially resolved infrared radiofluorescence: single-grain K-feldspar dating using CCD imaging. Geochronology 3, 299–319. https://doi.org/10.5194/gchron-3-299-2021</p> <p>R Core Team, 2021. R: A language and environment for statistical computing. https://www.r-project.org</p> <p> </p> <p> </p>
Effect of live cribwall on slope stability - modelling outputs
<p>These datasets contain outputs from a novel live cribwall model. The model assess the effect of a live cribwall on slope stability over time. The dataset contains Factor of Safety records under different plant cover and climate change scenarios. The model is still unpublished. For more detail, please get in touch aol3@gcu.ac.uk </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.