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          <dc:title>Morphological and molecular analyses reveal five new species of Hydnellum (Bankeraceae, Thelephorales) from the Changbai Mountains and Dabie Mountains, China</dc:title>
          <dc:creator>Yonglan Tuo (22010993)</dc:creator>
          <dc:subject>Mycology</dc:subject>
          <dc:subject>Hydnellum</dc:subject>
          <dc:subject>new taxa</dc:subject>
          <dc:subject>phylogeny</dc:subject>
          <dc:subject>China</dc:subject>
          <dc:description>&lt;p dir="ltr"&gt;This study describes five new species—H. aureomarginatum, H. coalescens, H. flavomarginatum, H. imbricatum, and H. jianense—collected from northeastern and central China, based on integrated morphological observations and phylogenetic analysis. Morphologically, H. aureomarginatum is characterized by a circular, depressed pileus that bears a golden-yellow zone on its inner margin, with the outermost edge of the pileus remaining white. H. coalescens is characterized by stipes of multiple basidiocarps that are fused or connate (forming a compound or interconnected basidiome) and a pileus covered with brown scales. H. flavomarginatum is characterized by a golden-yellow pileus margin and relatively large basidiospores. H. imbricatum is characterized by layered imbricate scales on the pileus and relatively long basidiospores. H. jianense is characterized by a plano-convex pileus covered with greyish-orange tomentum (which turns light brown when touched) and relatively small basidiospores. This study also compiles statistics on the main morphological characteristics, species distribution, and host information of the Hydnellum in China, deepening the understanding of the species diversity and distribution patterns of this genus.&lt;/p&gt;</dc:description>
          <dc:date>2026-04-19T10:44:23Z</dc:date>
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        <identifier>oai:figshare.com:article/32121478</identifier>
        <datestamp>2026-04-29T06:11:54Z</datestamp>
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          <dc:title>Supplementary table (S1) and figures (S1 and S2) from Unifying concepts in information-theoretic time-series analysis</dc:title>
          <dc:creator>Annie Gilmore Bryant (23808892)</dc:creator>
          <dc:creator>Oliver M Cliff (23808895)</dc:creator>
          <dc:creator>James M Shine (17100970)</dc:creator>
          <dc:creator>Ben D Fulcher (7520336)</dc:creator>
          <dc:creator>Joseph T. Lizier (7499861)</dc:creator>
          <dc:subject>Bioinformatics and computational biology not elsewhere classified</dc:subject>
          <dc:subject>Computational biology</dc:subject>
          <dc:subject>Biocomplexity</dc:subject>
          <dc:description>This PDF contains supplementary materials: Table S1 (computation times for all measures and estimators, applied to the left-hemisphere cerebral cortex fMRI time series of one HCP participant); Figure S1 (brain maps for all measures computed with the Gaussian density estimator); and Figure S2 (distribution of the computation times depicted in Table S1).</dc:description>
          <dc:date>2026-04-29T06:11:54Z</dc:date>
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          <dc:type>Journal contribution</dc:type>
          <dc:identifier>10.6084/m9.figshare.32121478.v1</dc:identifier>
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        <identifier>oai:figshare.com:article/32121475</identifier>
        <datestamp>2026-04-29T06:11:48Z</datestamp>
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          <dc:title>Distribution and community structure of riverine fish in two east-flowing rivers in Kenya</dc:title>
          <dc:creator>Japhet Kaadzo Tembo (23808877)</dc:creator>
          <dc:creator>Charles Maina Kihia (23808880)</dc:creator>
          <dc:creator>Nzula Kivuva Kitaka (23808883)</dc:creator>
          <dc:creator>Gordon O’Brien (16979388)</dc:creator>
          <dc:creator>Emmanuel Mbaru (23808886)</dc:creator>
          <dc:creator>Catherine Kariuki (23808889)</dc:creator>
          <dc:subject>Cell Biology</dc:subject>
          <dc:subject>Evolutionary Biology</dc:subject>
          <dc:subject>Ecology</dc:subject>
          <dc:subject>Inorganic Chemistry</dc:subject>
          <dc:subject>Environmental Sciences not elsewhere classified</dc:subject>
          <dc:subject>assemblage structure</dc:subject>
          <dc:subject>Athi-Galana-Sabaki</dc:subject>
          <dc:subject>catch per unit effort</dc:subject>
          <dc:subject>Ramisi River</dc:subject>
          <dc:description>&lt;p&gt;This study investigated the distribution, composition and habitat associations of freshwater eels in the Athi–Galana–Sabaki (Athi) and Ramisi Rivers using fortnightly fyke-net sampling conducted between April 2021 and March 2022. A total of 304 eels were recorded, with the Ramisi contributing 76% of individuals and the Athi River 24%. The assemblage was dominated by Anguilla bengalensis (75% of all eels), followed by A. bicolor (12%), A. mossambica (8%) and A. marmorata (5%). A. bengalensis was widespread and most abundant upstream in both rivers, indicating broad habitat plasticity. In contrast, A. bicolor and A. mossambica were largely restricted to lower, more saline reaches, while A. marmorata occurred primarily upstream. Eel biomass exceeded that of non-eel fish at most sites and differed significantly among locations, with highest values recorded in the upper Ramisi River (ANOVA: F = 2.9, p = 0.04). A one-way analysis of similarity (ANOSIM) indicated significant spatial differences in eel distribution between rivers and reaches (Global R = 0.49, p &lt; 0.05). Subsequent SIMPER analysis showed that these differences were primarily driven by variations in A. bengalensis (6%) and A. bicolor (1.12%). Multivariate analyses revealed significant spatial structuring of eel assemblages, with occurrence patterns strongly associated with conductivity, total dissolved solids, salinity, discharge, temperature, pH, and dissolved oxygen. These findings provide the first contemporary, reach-scale assessment of WIO eels in Kenyan rivers and highlight clear species-specific habitat associations along the river–estuary continuum. Conservation and fisheries management should prioritise maintaining longitudinal connectivity, protecting estuarine transition zones, sustaining water-quality gradients, and controlling invasive species to safeguard eel populations and their ecosystem functions.&lt;/p&gt;</dc:description>
          <dc:date>2026-04-29T06:11:48Z</dc:date>
          <dc:type>Text</dc:type>
          <dc:type>Journal contribution</dc:type>
          <dc:identifier>10.6084/m9.figshare.32121475.v1</dc:identifier>
          <dc:relation>https://figshare.com/articles/journal_contribution/Distribution_and_community_structure_of_riverine_fish_in_two_east-flowing_rivers_in_Kenya/32121475</dc:relation>
          <dc:rights>CC BY 4.0</dc:rights>
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        <identifier>oai:figshare.com:article/32121469</identifier>
        <datestamp>2026-04-29T06:11:21Z</datestamp>
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          <dc:title>Number of differentially expressed genes identified for each comparison</dc:title>
          <dc:creator>Hyo Jung Lee (23807680)</dc:creator>
          <dc:subject>Microbial genetics</dc:subject>
          <dc:subject>iron limitation conditions</dc:subject>
          <dc:subject>siderophore biosynthetic gene clusters</dc:subject>
          <dc:subject>Freshwater Bacteria</dc:subject>
          <dc:description>&lt;p dir="ltr"&gt;This file summarizes the number of differentially expressed genes identified for each experimental comparison between iron-replete and iron-limited conditions. The data include counts of upregulated and downregulated genes based on defined statistical thresholds.&lt;/p&gt;</dc:description>
          <dc:date>2026-04-29T06:11:21Z</dc:date>
          <dc:type>Dataset</dc:type>
          <dc:type>Dataset</dc:type>
          <dc:identifier>10.6084/m9.figshare.32121469.v1</dc:identifier>
          <dc:relation>https://figshare.com/articles/dataset/Number_of_differentially_expressed_genes_identified_for_each_comparison/32121469</dc:relation>
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        <identifier>oai:figshare.com:article/32121466</identifier>
        <datestamp>2026-04-29T00:00:00Z</datestamp>
        <setSpec>category_1</setSpec>
        <setSpec>category_4</setSpec>
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        <qdc:qualifieddc xmlns:qdc="figshare:oai:qdc" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xsi:schemaLocation="figshare:oai:qdc https://api.figshare.com/v2/static/figshare-oai-qdc.xsd">
          <dc:title>Semicrystalline
Polymacrolactone Networks via Disulfide
Chemistry: Synthesis, Cross-Linking, and Chemical Degradation Pathways</dc:title>
          <dc:creator>Nancy Ferrentino (20158527)</dc:creator>
          <dc:creator>Simona Russo (22191141)</dc:creator>
          <dc:creator>Fabio Di Giacomo (23808874)</dc:creator>
          <dc:creator>Federica Santulli (8629074)</dc:creator>
          <dc:creator>Andrea Sorrentino (1511869)</dc:creator>
          <dc:creator>Mina Mazzeo (1841917)</dc:creator>
          <dc:creator>Daniela Pappalardo (1853299)</dc:creator>
          <dc:subject>Biophysics</dc:subject>
          <dc:subject>Biochemistry</dc:subject>
          <dc:subject>Biotechnology</dc:subject>
          <dc:subject>Immunology</dc:subject>
          <dc:subject>Biological Sciences not elsewhere classified</dc:subject>
          <dc:subject>Chemical Sciences not elsewhere classified</dc:subject>
          <dc:subject>Physical Sciences not elsewhere classified</dc:subject>
          <dc:subject>uv light irradiation</dc:subject>
          <dc:subject>structural order within</dc:subject>
          <dc:subject>possibly reprocessable plastics</dc:subject>
          <dc:subject>ftir spectroscopy confirmed</dc:subject>
          <dc:subject>differential scanning calorimetry</dc:subject>
          <dc:subject>crystalline domains resulted</dc:subject>
          <dc:subject>chemical degradation pathways</dc:subject>
          <dc:subject>180 ° c</dc:subject>
          <dc:subject>network formation process</dc:subject>
          <dc:subject>highest network stiffness</dc:subject>
          <dc:subject>rheological analysis revealed</dc:subject>
          <dc:subject>dependent viscoelastic response</dc:subject>
          <dc:subject>performance polymer networks</dc:subject>
          <dc:subject>achieved via reaction</dc:subject>
          <dc:subject>50 copolymer achieving</dc:subject>
          <dc:subject>retained significant crystallinity</dc:subject>
          <dc:subject>polymer network</dc:subject>
          <dc:subject>significant degree</dc:subject>
          <dc:subject>rheological time</dc:subject>
          <dc:subject>dependent increase</dc:subject>
          <dc:subject>polymer matrix</dc:subject>
          <dc:subject>versatile platform</dc:subject>
          <dc:subject>sweep experiments</dc:subject>
          <dc:subject>semicrystalline polyesters</dc:subject>
          <dc:subject>results demonstrated</dc:subject>
          <dc:subject>renewable thermoset</dc:subject>
          <dc:subject>next generation</dc:subject>
          <dc:subject>materials science</dc:subject>
          <dc:subject>link density</dc:subject>
          <dc:subject>like materials</dc:subject>
          <dc:subject>hexadecenlactone -&lt;</dc:subject>
          <dc:subject>herein described</dc:subject>
          <dc:subject>epoxidized poly</dc:subject>
          <dc:subject>dsc ),</dc:subject>
          <dc:subject>different compositions</dc:subject>
          <dc:subject>creating high</dc:subject>
          <dc:subject>co &lt;/</dc:subject>
          <dc:subject>central challenge</dc:subject>
          <dc:description>The development of sustainable polymers is a central
challenge
in advancing circular economy strategies for materials science. In
this framework, the synthesis of disulfide-cross-linked polymer networks
derived from epoxidized poly(ω-6-hexadecenlactone-&lt;i&gt;co&lt;/i&gt;-ω-pentadecalactone) (P6HDL/PDL), a family of biobased, semicrystalline
polyesters, is herein described. Cross-linking was achieved via reaction
with 4,4′-dithiodibutyric acid (DTDB), introducing disulfide
bonds into the polymer matrix. The network formation process was monitored
through rheological time-sweep experiments and differential scanning
calorimetry (DSC), while FTIR spectroscopy confirmed the completion
of the epoxy–acid reaction. Rheological analysis revealed a
composition-dependent increase in complex viscosity upon cross-linking
at 180 °C, with the 50/50 copolymer achieving the highest network
stiffness. Notably, copolymers with different compositions (P6HDL/PDL
= 50/50 and 25/75) retained significant crystallinity after cross-linking.
This coexistence of disulfide bonds and crystalline domains resulted
in a composition-dependent viscoelastic response while maintaining
a significant degree of structural order within the polymer network.
The copolymers’ structure allowed tuning of cross-link density
and crystallinity, offering a versatile platform for the design of
renewable thermoset-like materials. The cleavage of disulfide bonds
was achieved by treatment with 2-mercaptoethanol or 2-hydroxyethyl
disulfide by UV light irradiation. These results demonstrated the
feasibility of creating high-performance polymer networks from macrolactone-based
polyesters using dynamic disulfide chemistry and laid the groundwork
for the next generation of sustainable and possibly reprocessable
plastics.</dc:description>
          <dc:date>2026-04-29T00:00:00Z</dc:date>
          <dc:type>Text</dc:type>
          <dc:type>Journal contribution</dc:type>
          <dc:identifier>10.1021/polymscitech.6c00021.s001</dc:identifier>
          <dc:relation>https://figshare.com/articles/journal_contribution/Semicrystalline_Polymacrolactone_Networks_via_Disulfide_Chemistry_Synthesis_Cross-Linking_and_Chemical_Degradation_Pathways/32121466</dc:relation>
          <dc:rights>CC BY-NC 4.0</dc:rights>
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      <header>
        <identifier>oai:figshare.com:article/32121463</identifier>
        <datestamp>2026-04-29T00:00:00Z</datestamp>
        <setSpec>category_4</setSpec>
        <setSpec>category_7</setSpec>
        <setSpec>category_21</setSpec>
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      <metadata>
        <qdc:qualifieddc xmlns:qdc="figshare:oai:qdc" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xsi:schemaLocation="figshare:oai:qdc https://api.figshare.com/v2/static/figshare-oai-qdc.xsd">
          <dc:title>Enhanced Hydrogen
Storage in Metal Organic Framework/Graphene
Oxide Composites: Experimental Characterization and Molecular Simulations</dc:title>
          <dc:creator>Mohamed Hammad Elsayed (10479733)</dc:creator>
          <dc:creator>Mohamed M. Elsenety (14517217)</dc:creator>
          <dc:creator>Rawan A. Al-Qahtani (23808865)</dc:creator>
          <dc:creator>Amr Sabbah (11059091)</dc:creator>
          <dc:creator>Mohamed Essalhi (9564317)</dc:creator>
          <dc:creator>Ahmed F. Saber (6049187)</dc:creator>
          <dc:creator>Danah Faisal Almalki (23808868)</dc:creator>
          <dc:creator>Kuei-Hsien Chen (368666)</dc:creator>
          <dc:creator>Li-Chyong Chen (1596040)</dc:creator>
          <dc:creator>Mahmoud Abdelnaby (23808871)</dc:creator>
          <dc:subject>Biochemistry</dc:subject>
          <dc:subject>Medicine</dc:subject>
          <dc:subject>Biotechnology</dc:subject>
          <dc:subject>tunable nanoporous architectures</dc:subject>
          <dc:subject>situ growth method</dc:subject>
          <dc:subject>scanning electron microscopy</dc:subject>
          <dc:subject>rapidly expanding class</dc:subject>
          <dc:subject>open metal sites</dc:subject>
          <dc:subject>metal organic framework</dc:subject>
          <dc:subject>hybrid framework exhibits</dc:subject>
          <dc:subject>1 bar reveal</dc:subject>
          <dc:subject>highest storage enhancement</dc:subject>
          <dc:subject>ray photoelectron spectroscopy</dc:subject>
          <dc:subject>high surface area</dc:subject>
          <dc:subject>density functional theory</dc:subject>
          <dc:subject>optimal go loading</dc:subject>
          <dc:subject>go interfacial regions</dc:subject>
          <dc:subject>either pristine go</dc:subject>
          <dc:subject>enhanced hydrogen storage</dc:subject>
          <dc:subject>maximizing hydrogen adsorption</dc:subject>
          <dc:subject>hydrogen adsorption isotherms</dc:subject>
          <dc:subject>study integrates mofs</dc:subject>
          <dc:subject>significantly higher h</dc:subject>
          <dc:subject>graphene oxide composites</dc:subject>
          <dc:subject>2 &lt;/ sub</dc:subject>
          <dc:subject>hydrogen storage capacity</dc:subject>
          <dc:subject>go composite exhibits</dc:subject>
          <dc:subject>hydrogen storage</dc:subject>
          <dc:subject>hydrogen adsorption</dc:subject>
          <dc:subject>graphene oxide</dc:subject>
          <dc:subject>go surface</dc:subject>
          <dc:subject>ray diffraction</dc:subject>
          <dc:subject>functional groups</dc:subject>
          <dc:subject>go composite</dc:subject>
          <dc:subject>uptake capacity</dc:subject>
          <dc:subject>synthesized via</dc:subject>
          <dc:subject>synergistic effect</dc:subject>
          <dc:subject>recent years</dc:subject>
          <dc:subject>promising candidates</dc:subject>
          <dc:subject>numerous mofs</dc:subject>
          <dc:subject>natural gas</dc:subject>
          <dc:subject>molecules occupy</dc:subject>
          <dc:subject>microporous materials</dc:subject>
          <dc:subject>microporous channels</dc:subject>
          <dc:subject>level insights</dc:subject>
          <dc:subject>integrated structure</dc:subject>
          <dc:subject>extensively characterized</dc:subject>
          <dc:subject>experimental characterization</dc:subject>
          <dc:subject>effective materials</dc:subject>
          <dc:subject>composite consisting</dc:subject>
          <dc:subject>77 k</dc:subject>
          <dc:subject>34 kcal</dc:subject>
          <dc:subject>25 kcal</dc:subject>
          <dc:description>Metal–organic frameworks (MOFs) constitute a rapidly
expanding
class of microporous materials. In recent years, numerous MOFs with
tunable nanoporous architectures have been developed as promising
candidates for natural gas and hydrogen storage. To enhance the hydrogen
storage capacity of MOFs while reducing the overall cost, this study
integrates MOFs with cost-effective materials that provide additional
active sites for hydrogen adsorption. Here, we report the development
of a composite consisting of graphene oxide (GO) and Ni-based MOF-74
(Ni-MOF-74), which combines the high surface area and functional groups
of GO with the extensive porosity and open metal sites of the MOF.
The Ni-MOF-74/GO composite was synthesized via an in situ growth method
and extensively characterized by using transmission electron microscopy,
scanning electron microscopy, Fourier transform infrared spectroscopy,
powder X-ray diffraction, X-ray photoelectron spectroscopy, and Brunauer–Emmett–Teller
surface area analysis to confirm its integrated structure and porosity.
Hydrogen adsorption isotherms at 77 K and up to 1 bar reveal that
the Ni-MOF-74/GO composite exhibits a significantly higher H&lt;sub&gt;2&lt;/sub&gt; uptake capacity than either pristine GO or Ni-MOF-74 alone. Notably,
the composite with an optimal GO loading (10 wt %) achieves the highest
storage enhancement, demonstrating a synergistic effect between GO
and the MOF in maximizing hydrogen adsorption. Density functional
theory (DFT) and Monte Carlo simulations provided molecular-level
insights, indicating that H&lt;sub&gt;2&lt;/sub&gt; molecules occupy both the
microporous channels of Ni-MOF-74 and the GO surface, particularly
at the Ni-MOF-74/GO interfacial regions. This hybrid framework exhibits
a slightly stronger hydrogen adsorption energy (−3.34 kcal/mol)
compared to Ni-MOF-74 alone (−3.25 kcal/mol), with minimal
structural distortion upon H&lt;sub&gt;2&lt;/sub&gt; uptake.</dc:description>
          <dc:date>2026-04-29T00:00:00Z</dc:date>
          <dc:type>Text</dc:type>
          <dc:type>Journal contribution</dc:type>
          <dc:identifier>10.1021/acs.langmuir.6c01098.s001</dc:identifier>
          <dc:relation>https://figshare.com/articles/journal_contribution/Enhanced_Hydrogen_Storage_in_Metal_Organic_Framework_Graphene_Oxide_Composites_Experimental_Characterization_and_Molecular_Simulations/32121463</dc:relation>
          <dc:rights>CC BY-NC 4.0</dc:rights>
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    <record>
      <header>
        <identifier>oai:figshare.com:article/32121439</identifier>
        <datestamp>2026-04-29T06:07:51Z</datestamp>
        <setSpec>category_24361</setSpec>
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        <setSpec>item_type_1</setSpec>
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      </header>
      <metadata>
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          <dc:title>Volcano plots for differentially expressed genes across experimental contrasts</dc:title>
          <dc:creator>Hyo Jung Lee (23807680)</dc:creator>
          <dc:subject>Microbial genetics</dc:subject>
          <dc:subject>iron limitation conditions</dc:subject>
          <dc:subject>siderophore biosynthetic gene clusters</dc:subject>
          <dc:subject>Freshwater bacteria</dc:subject>
          <dc:description>&lt;p dir="ltr"&gt;This file contains volcano plots illustrating differentially expressed genes across experimental contrasts between iron-replete and iron-limited conditions. Each plot displays the relationship between fold change and statistical significance, highlighting significantly upregulated and downregulated genes.&lt;/p&gt;</dc:description>
          <dc:date>2026-04-29T06:07:51Z</dc:date>
          <dc:type>Image</dc:type>
          <dc:type>Figure</dc:type>
          <dc:identifier>10.6084/m9.figshare.32121439.v1</dc:identifier>
          <dc:relation>https://figshare.com/articles/figure/Volcano_plots_for_differentially_expressed_genes_across_experimental_contrasts/32121439</dc:relation>
          <dc:rights>CC BY 4.0</dc:rights>
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    <record>
      <header>
        <identifier>oai:figshare.com:article/32121460</identifier>
        <datestamp>2026-04-29T00:00:00Z</datestamp>
        <setSpec>category_1</setSpec>
        <setSpec>category_4</setSpec>
        <setSpec>category_19</setSpec>
        <setSpec>category_21</setSpec>
        <setSpec>category_734</setSpec>
        <setSpec>category_873</setSpec>
        <setSpec>category_931</setSpec>
        <setSpec>portal_63</setSpec>
        <setSpec>item_type_6</setSpec>
        <setSpec>month_year_04_2026</setSpec>
      </header>
      <metadata>
        <qdc:qualifieddc xmlns:qdc="figshare:oai:qdc" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xsi:schemaLocation="figshare:oai:qdc https://api.figshare.com/v2/static/figshare-oai-qdc.xsd">
          <dc:title>Chemistry-Enhanced
Diffusion-Based Framework for Small-to-Large
Molecular Conformation Generation</dc:title>
          <dc:creator>Yifei Zhu (3450488)</dc:creator>
          <dc:creator>Jiahui Zhang (3163929)</dc:creator>
          <dc:creator>Jiawei Peng (9733744)</dc:creator>
          <dc:creator>Mengge Li (3775978)</dc:creator>
          <dc:creator>Chao Xu (201376)</dc:creator>
          <dc:creator>Zhenggang Lan (1278597)</dc:creator>
          <dc:subject>Biophysics</dc:subject>
          <dc:subject>Biochemistry</dc:subject>
          <dc:subject>Pharmacology</dc:subject>
          <dc:subject>Biotechnology</dc:subject>
          <dc:subject>Biological Sciences not elsewhere classified</dc:subject>
          <dc:subject>Chemical Sciences not elsewhere classified</dc:subject>
          <dc:subject>Information Systems not elsewhere classified</dc:subject>
          <dc:subject>scratch without seeing</dc:subject>
          <dc:subject>maintaining high scalability</dc:subject>
          <dc:subject>embedding chemical principles</dc:subject>
          <dc:subject>chemically valid fragments</dc:subject>
          <dc:subject>broad configurational coverage</dc:subject>
          <dc:subject>although recent machine</dc:subject>
          <dc:subject>realistic polyatomic molecules</dc:subject>
          <dc:subject>based strategy eliminates</dc:subject>
          <dc:subject>chemically rational structures</dc:subject>
          <dc:subject>large molecular structures</dc:subject>
          <dc:subject>stol assembles molecules</dc:subject>
          <dc:subject>diffusion model trained</dc:subject>
          <dc:subject>molecule training data</dc:subject>
          <dc:subject>target molecules</dc:subject>
          <dc:subject>molecule data</dc:subject>
          <dc:subject>introduce stol</dc:subject>
          <dc:subject>based framework</dc:subject>
          <dc:subject>3d structures</dc:subject>
          <dc:subject>style fashion</dc:subject>
          <dc:subject>smiles input</dc:subject>
          <dc:subject>small molecules</dc:subject>
          <dc:subject>predicting large</dc:subject>
          <dc:subject>key steps</dc:subject>
          <dc:subject>free generation</dc:subject>
          <dc:subject>enhanced diffusion</dc:subject>
          <dc:subject>enables rapid</dc:subject>
          <dc:subject>diverse conformations</dc:subject>
          <dc:subject>dft calculations</dc:subject>
          <dc:subject>comparable size</dc:subject>
          <dc:description>Obtaining 3D conformations of realistic polyatomic molecules
at
the quantum chemistry level remains challenging, and although recent
machine-learning advances offer promise, predicting large-molecule
structures still requires substantial computational effort. Here we
introduce StoL, a diffusion-model-based framework that enables rapid
and knowledge-free generation of large molecular structures from small-molecule
data. Remarkably, StoL assembles molecules in a LEGO-style fashion
from scratch without seeing the target molecules or any structures
of comparable size during training. Given an SMILES input, it decomposes
the molecule into chemically valid fragments, generates their 3D structures
with a diffusion model trained on small molecules, and assembles them
into diverse conformations. This fragment-based strategy eliminates
the need for large-molecule training data while maintaining high scalability
and transferability. By embedding chemical principles into key steps,
StoL ensures faster convergence, chemically rational structures, and
broad configurational coverage, as confirmed against DFT calculations.</dc:description>
          <dc:date>2026-04-29T00:00:00Z</dc:date>
          <dc:type>Text</dc:type>
          <dc:type>Journal contribution</dc:type>
          <dc:identifier>10.1021/acs.jpclett.5c04019.s001</dc:identifier>
          <dc:relation>https://figshare.com/articles/journal_contribution/Chemistry-Enhanced_Diffusion-Based_Framework_for_Small-to-Large_Molecular_Conformation_Generation/32121460</dc:relation>
          <dc:rights>CC BY-NC 4.0</dc:rights>
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    <record>
      <header>
        <identifier>oai:figshare.com:article/32121265</identifier>
        <datestamp>2026-04-01T00:00:00Z</datestamp>
        <setSpec>category_31817</setSpec>
        <setSpec>category_31973</setSpec>
        <setSpec>category_32963</setSpec>
        <setSpec>category_33053</setSpec>
        <setSpec>category_33464</setSpec>
        <setSpec>portal_1005</setSpec>
        <setSpec>item_type_6</setSpec>
        <setSpec>month_year_04_2026</setSpec>
      </header>
      <metadata>
        <qdc:qualifieddc xmlns:qdc="figshare:oai:qdc" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xsi:schemaLocation="figshare:oai:qdc https://api.figshare.com/v2/static/figshare-oai-qdc.xsd">
          <dc:title>Scalable All Nanotube Carbon Nanotube–Boron Nitride Nanotube–Carbon Nanotube Supercapacitors for Enhanced Structural Energy Storage</dc:title>
          <dc:creator>BJ Mapleback (18566350)</dc:creator>
          <dc:creator>VH Dao (23808850)</dc:creator>
          <dc:creator>AN Rider (18566353)</dc:creator>
          <dc:creator>Patrick Howlett (13088661)</dc:creator>
          <dc:subject>Chemical sciences</dc:subject>
          <dc:subject>Physical chemistry</dc:subject>
          <dc:subject>Engineering</dc:subject>
          <dc:subject>Chemical engineering</dc:subject>
          <dc:subject>Materials engineering</dc:subject>
          <dc:subject>boron nitride nanotubes</dc:subject>
          <dc:subject>carbon nanotubes</dc:subject>
          <dc:subject>Electrochemistry</dc:subject>
          <dc:subject>ionic liquids</dc:subject>
          <dc:subject>Materials Science</dc:subject>
          <dc:subject>Materials Science, Multidisciplinary</dc:subject>
          <dc:subject>Physical Sciences</dc:subject>
          <dc:subject>Science &amp; Technology</dc:subject>
          <dc:subject>supercapacitors</dc:subject>
          <dc:subject>Technology</dc:subject>
          <dc:description>
                    Composite structural supercapacitors (SSCs) seek to offer improved size, weight, and power energy storage to mobile devices or transportation vehicles. This technology, similar to the development cycle of high‐performance batteries, requires significant materials development tailored to the supercapacitors requirements. Several electrode materials have been developed which offer good energy and power performance, but are limited in their effectiveness due to the lack of an advanced separator material suitable for high‐performance composite materials processing. Currently, glass fiber cloths are the only feasible separator material for SSCs. The present research effort examines an alternative to separator materials. Boron nitride nanotube (BNNT) films have been deposited onto a single side of carbon nanotube (CNT) mat electrodes using vacuum filtration. Thin and uniform BNNT films show good coverage, but may exhibit thickness variations due to the flow properties of the underlying CNT mat. BNNT coverage and mechanical robustness are improved at significantly reduced thicknesses by decreasing the amount of BNNT and adding long‐chain polymer binders. Careful selection of polymers and ionic liquid electrolyte enables production of reliable, high‐rate performance supercapacitors with specific energy and power of 11.5 Wh kg
                    −
                    1
                    at 0.137 kW kg
                    −
                    1
                    and 7.87 Wh kg
                    −
                    1
                    at 2.69 kW kg
                    −
                    1
                    with higher charge–discharge rates. These CNT–BNNT combined electrode and separator components were integrated into a high‐performance fiber‐reinforced polymer composite and cured using industrial autoclave‐cure techniques. The SSC provided excellent electrochemical performance with specific energy and power of 12.8 Wh kg
                    −
                    1
                    at 0.0985 kW kg
                    −
                    1
                    and retained 3.57 Wh kg
                    −1
                    at 1.31 kW kg
                    −1
                    at higher charge–discharge rates, normalizing the combined electrode mass of the CNT–BNNT cathode and anode. The device also shows reliable long‐term performance with 70% capacity retention after 5000 charge–discharge cycles from 0 to 3.3 V at high rates of 0.5 A g
                    −1
                    .
                  </dc:description>
          <dc:date>2026-04-01T00:00:00Z</dc:date>
          <dc:type>Text</dc:type>
          <dc:type>Journal contribution</dc:type>
          <dc:identifier>10.26187/deakin.32121265</dc:identifier>
          <dc:relation>https://figshare.com/articles/journal_contribution/Scalable_All_Nanotube_Carbon_Nanotube_Boron_Nitride_Nanotube_Carbon_Nanotube_Supercapacitors_for_Enhanced_Structural_Energy_Storage/32121265</dc:relation>
          <dc:rights>All Rights Reserved</dc:rights>
          <dcterms:hasPart>https://ndownloader.figshare.com/files/64088398</dcterms:hasPart>
          <dcterms:hasVersion>1</dcterms:hasVersion>
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    <record>
      <header>
        <identifier>oai:figshare.com:article/32121457</identifier>
        <datestamp>2026-04-29T00:00:00Z</datestamp>
        <setSpec>category_1</setSpec>
        <setSpec>category_4</setSpec>
        <setSpec>category_12</setSpec>
        <setSpec>category_14</setSpec>
        <setSpec>category_24</setSpec>
        <setSpec>category_64</setSpec>
        <setSpec>category_272</setSpec>
        <setSpec>category_734</setSpec>
        <setSpec>category_873</setSpec>
        <setSpec>category_915</setSpec>
        <setSpec>portal_63</setSpec>
        <setSpec>item_type_6</setSpec>
        <setSpec>month_year_04_2026</setSpec>
      </header>
      <metadata>
        <qdc:qualifieddc xmlns:qdc="figshare:oai:qdc" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xsi:schemaLocation="figshare:oai:qdc https://api.figshare.com/v2/static/figshare-oai-qdc.xsd">
          <dc:title>Simultaneous Cross-Linking
Olefin Polymerization Technology
for Heterophasic Copolymerization of Propylene toward High-Rubber-Content
TPO: Critical Role of the Concentration of Nonconjugated α,ω-Diene
Cross-Linker</dc:title>
          <dc:creator>Yiming Wang (163598)</dc:creator>
          <dc:creator>Jin-Yong Dong (2257189)</dc:creator>
          <dc:subject>Biophysics</dc:subject>
          <dc:subject>Biochemistry</dc:subject>
          <dc:subject>Cell Biology</dc:subject>
          <dc:subject>Molecular Biology</dc:subject>
          <dc:subject>Evolutionary Biology</dc:subject>
          <dc:subject>Cancer</dc:subject>
          <dc:subject>Environmental Sciences not elsewhere classified</dc:subject>
          <dc:subject>Biological Sciences not elsewhere classified</dc:subject>
          <dc:subject>Chemical Sciences not elsewhere classified</dc:subject>
          <dc:subject>Physical Sciences not elsewhere classified</dc:subject>
          <dc:subject>work clearly identifies</dc:subject>
          <dc:subject>performance polyolefin elastomers</dc:subject>
          <dc:subject>optimal operational window</dc:subject>
          <dc:subject>linker utilizing 1</dc:subject>
          <dc:subject>enabling synergistic increases</dc:subject>
          <dc:subject>achieving integrated control</dc:subject>
          <dc:subject>universal triconcentration framework</dc:subject>
          <dc:subject>topological evolution mechanisms</dc:subject>
          <dc:subject>linear chain structures</dc:subject>
          <dc:subject>propylene toward high</dc:subject>
          <dc:subject>model nonconjugated α</dc:subject>
          <dc:subject>low dd loadings</dc:subject>
          <dc:subject>linking olefin polymerization</dc:subject>
          <dc:subject>gel network formation</dc:subject>
          <dc:subject>w &lt;/ sub</dc:subject>
          <dc:subject>induction concentration (&lt;</dc:subject>
          <dc:subject>induction &lt;/ sub</dc:subject>
          <dc:subject>gelation concentration (&lt;</dc:subject>
          <dc:subject>nonconjugated α</dc:subject>
          <dc:subject>gel network</dc:subject>
          <dc:subject>&gt;&lt; sub</dc:subject>
          <dc:subject>predictive framework</dc:subject>
          <dc:subject>phase evolution</dc:subject>
          <dc:subject>critical concentration</dc:subject>
          <dc:subject>chain relaxation</dc:subject>
          <dc:subject>chain construction</dc:subject>
          <dc:subject>chain branching</dc:subject>
          <dc:subject>c &lt;/</dc:subject>
          <dc:subject>lcb (&lt;</dc:subject>
          <dc:subject>study elucidates</dc:subject>
          <dc:subject>simultaneous cross</dc:subject>
          <dc:subject>results indicate</dc:subject>
          <dc:subject>producing high</dc:subject>
          <dc:subject>parameters establish</dc:subject>
          <dc:subject>investigative linchpin</dc:subject>
          <dc:subject>heterophasic copolymerization</dc:subject>
          <dc:subject>critical role</dc:subject>
          <dc:subject>content tpo</dc:subject>
          <dc:subject>construction mechanism</dc:subject>
          <dc:subject>branched architectures</dc:subject>
          <dc:description>Utilizing 1,9-decadiene (DD) as a model nonconjugated
α,ω-diene,
high-rubber-content TPO was prepared through simultaneous cross-linking
olefin polymerization (SCOP) based on Ziegler–Natta catalysts.
Taking the counterintuitive particle adhesion observed at low DD loadings
as the investigative linchpin, this study elucidates the long-chain
branching (LCB) construction mechanism in SCOP processes and proposes
a universal triconcentration framework: the critical concentration
for LCB (&lt;i&gt;C&lt;/i&gt;&lt;sub&gt;critical&lt;/sub&gt;), the induction concentration
(&lt;i&gt;C&lt;/i&gt;&lt;sub&gt;induction&lt;/sub&gt;), and the gelation concentration
(&lt;i&gt;C&lt;/i&gt;&lt;sub&gt;gel&lt;/sub&gt;). These parameters establish a comprehensive
topological evolution framework describing the transition from linear
chain structures to branched architectures and eventually to gel network
formation, achieving integrated control and close correlation across
three scales: chain construction, chain relaxation, and phase evolution.
The results indicate that below &lt;i&gt;C&lt;/i&gt;&lt;sub&gt;critical&lt;/sub&gt;, chain transfer dominates, leading to reduced molecular weight (&lt;i&gt;M&lt;/i&gt;&lt;sub&gt;w&lt;/sub&gt;). Beyond &lt;i&gt;C&lt;/i&gt;&lt;sub&gt;induction&lt;/sub&gt;, effective intermolecular cross-linking is triggered, enabling synergistic
increases in &lt;i&gt;M&lt;/i&gt;&lt;sub&gt;w&lt;/sub&gt; and long-chain branching
density (&lt;i&gt;D&lt;/i&gt;&lt;sub&gt;LCB&lt;/sub&gt;). Upon &lt;i&gt;C&lt;/i&gt;&lt;sub&gt;gel&lt;/sub&gt;, a gel network is formed. This work clearly identifies
the optimal operational window for producing high-rubber-content TPO
via SCOP technology as lying between &lt;i&gt;C&lt;/i&gt;&lt;sub&gt;induction&lt;/sub&gt; and &lt;i&gt;C&lt;/i&gt;&lt;sub&gt;gel&lt;/sub&gt;, providing fundamental insights
into topological evolution mechanisms and establishing a predictive
framework that may guide future process design for high-performance
polyolefin elastomers.</dc:description>
          <dc:date>2026-04-29T00:00:00Z</dc:date>
          <dc:type>Text</dc:type>
          <dc:type>Journal contribution</dc:type>
          <dc:identifier>10.1021/acs.iecr.6c00951.s001</dc:identifier>
          <dc:relation>https://figshare.com/articles/journal_contribution/Simultaneous_Cross-Linking_Olefin_Polymerization_Technology_for_Heterophasic_Copolymerization_of_Propylene_toward_High-Rubber-Content_TPO_Critical_Role_of_the_Concentration_of_Nonconjugated_-Diene_Cross-Linker/32121457</dc:relation>
          <dc:rights>CC BY-NC 4.0</dc:rights>
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