Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
5
datasets available to search
ShareScore release 0.9.0
Dataset results
5 results for “mixed conductors”
Bridging length scales in organic mixed ionic-electronic conductors through internal strain and mesoscale dynamics
<p>Understanding structural and dynamic properties of inherently disordered systems at the mesoscale is crucial. This is particularly important in organic mixed ionic-electronic conductors (OMIECs), which undergo significant and complex structural changes when operated in electrolyte. In this study, we investigate the mesoscale strain, reversibility, and dynamics of a model OMIEC material under external electrochemical potential using operando X-ray photon correlation spectroscopy. Our results reveal mesoscale strain and structural hysteresis that depend on the sample's cycling history, establishing a comprehensive kinetic sequence bridging the macroscopic and microscopic behaviors of OMIECs. Furthermore, we uncover equilibrium and non-equilibrium dynamics of charge carriers and material doping states, highlighting the unexpected coupling between charge carrier dynamics and mesoscale order. These findings advance our understanding of the structure-dynamics-function relationships in OMIECs, opening pathways for designing and engineering materials with improved performance and functionality in non-equilibrium states during device operation.</p>
Data from: Direct quantification of ion composition and mobility in organic mixed ionic-electronic conductors
<p>Ion transport in organic mixed ionic-electronic conductors (OMIECs) is crucial due to its direct impact on device response time and fundamental operating mechanisms but are often assessed indirectly or rely on extra assumptions. Operando X-ray fluorescence (XRF) is a powerful, direct probe useful for elemental characterization of bulk OMIECs, and was employed to directly quantify ion composition and mobility in a model OMIEC, PEDOT:PSS, during device operation. The first cycle revealed slow electrowetting and cation-proton exchange. Subsequent cycles showed rapid response with minor cation fluctuation (~5%). Comparison with optical-tracked electrochromic fronts revealed a mesoscale structure dependent proton transport. The calculated effective ion mobility demonstrated thickness-dependent behavior, emphasizing an interfacial ion transport pathway with a higher mobile ion density. The decoupling of bulk and interfacial effects on ion mobility, and the decoupling of cation and proton transport contributes to our understanding of ion transport in conventional and emerging OMIEC-based devices, and has broader implications for ion transport in other ionic conductors writ large.</p>
Bridging length scales in organic mixed ionic-electronic conductors through internal strain and mesoscale dynamics
Open the record for dataset details and reuse information.
Data from: Direct quantification of ion composition and mobility in organic mixed ionic-electronic conductors
Open the record for dataset details and reuse information.
Data and Code For : "The Hierarchical Structure of Organic Mixed Ionic Electronic Conductors and Its Evolution in Water."
<p>The repository contains the principal 4D-STEM datasets and code used in the paper:</p> <p>"The Hierarchical Structure of Organic Mixed Ionic Electronic Conductors and Its Evolution in Water."</p> <p> </p> <p>* The measured and analyzed material is p(g3T2).</p> <p>* The code can be adjusted and used for the analysis of other conjugated polymers.</p> <p>* It should be noted that newer py4DSTEM versions with additional capabilities were released since the paper was </p> <p>submitted. </p> <p> </p> <p><strong>Contents:</strong></p> <p><strong>1. 4D-STEM_DATA_OMIECs.zip : </strong></p> <p><strong>4D-STEM data : </strong></p> <ul> <li>Dry_CL_2p1.dm4. </li> </ul> <p>scanned area [pixels]: 100x100, step size: 20 nm, CL: 2.1, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 27 ms, spot size: 6, mono: 20, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li> Calibrant_Dry_CL_2p1.dm4</li> </ul> <p>scanned area [pixels]: 45x48, step size: 10 nm, CL: 2.1, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 13 ms, spot size: 6, mono: 40, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Dry_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 100x100, step size: 20 nm, CL: 2.7, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 27 ms, spot size: 6, mono: 20, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Calibrant_Dry_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 45x48, step size: 10 nm, CL: 2.1, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 13 ms, spot size: 6, mono: 40, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Hydrated_Water_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 100x100, step size: 15 nm, CL: 2.7, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 27 ms, spot size: 6, mono: 25, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Calibrant_Hydrated_Water_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 50x50, step size: 10 nm, CL: 2.1, c2 ca: 10 um, alpha 0.17 mrad, bin = 2,</p> <p>exposure time: 13 ms, spot size: 6, mono: 46, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Hydrated_NaCl_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 100x100, step size: 20 nm, CL: 2.7, c2 ca: 10 um, alpha 0.18 mrad, bin = 4,</p> <p>exposure time: 13 ms, spot size: 6, mono: 15, E(extraction voltage): 300 kV, temprature: LN. </p> <ul> <li>Calibrant_Hydrated_NaCl_CL_2p7.dm4</li> </ul> <p>scanned area [pixels]: 50x50, step size: 10 nm, CL: 2.1, c2 ca: 10 um, alpha 0.18 mrad, bin = 4,</p> <p>exposure time: 13 ms, spot size: 6, mono: 80, E(extraction voltage): 300 kV, temprature: LN.</p> <p> </p> <p><strong>2. Notebooks.zip : </strong></p> <p><strong>Jupyter Lab Notebooks : </strong></p> <ul> <li>1A_pg3T2_dry_LN_CL2p1.ipynb. </li> </ul> <p>Analysis of dry film using CL 2.1.</p> <p>Goes with datasets: Dry_CL_2p1.dm4 and Calibrant_Dry_CL_2p1.dm4. </p> <ul> <li>1A_pg3T2_dry_LN_CL2p7.ipynb</li> </ul> <p>Analysis of dry film using CL 2.7.</p> <p>Goes with datasets: Dry_CL_2p7.dm4 and Calibrant_Dry_CL_2p7.dm4. </p> <ul> <li>1B_pg3T2_water_LN_CL2p7.ipynb</li> </ul> <p>Analysis of hydrated in water film using CL 2.7.</p> <p>Goes with datasets: Hydrated_Water_CL_2p7.dm4 and Calibrant_Hydrated_Water_CL_2p7.dm4. </p> <ul> <li>1C_pg3T2_NaCl_LN_CL2p7. </li> </ul> <p>Analysis of hydrated in 0.1 M NaCl(aq) film using CL 2.7.</p> <p>Goes with datasets: Hydrated_NaCl_CL_2p7.dm4 and Calibrant_Hydrated_NaCl_CL_2p7.dm4.</p> <ul> <li>aux_func.py: </li> </ul> <p>Contains auxilary functions and required for running the other notebooks.</p>
ScienceDex guides
Understand access before you commit
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.