The separation of Helium gas from natural gas is challenging but highly important. MIL‐116(Ga), a “non‐porous” metal–organic framework is used as a molecular sieve to separate He from CH4. Druse‐like MIL‐116(Ga) particles are integrated into polysulfone mixed matrix membranes.
Ayisha Komal +10 more
wiley +1 more source
Granular Jamming in Soft Robotics: Simulation Frameworks and Emerging Possibilities-Review. [PDF]
Hrehova S +3 more
europepmc +1 more source
Laser‐Induced Graphene from Waste Almond Shells
Almond shells, an abundant agricultural by‐product, are repurposed to create a fully bioderived almond shell/chitosan composite (ASC) degradable in soil. ASC is converted into laser‐induced graphene (LIG) by laser scribing and proposed as a substrate for transient electronics.
Yulia Steksova +9 more
wiley +1 more source
Controlled syntheses of lanthanide coordination polymers based on the dihydroxybenzoquinone (DHBQ) organic linker afforded large single crystals of Ln‐DHBQ CPs (Ln = Yb, Nd). A novel structural variant of Yb‐DHBQ is identified by means of single crystal diffraction analysis.
Marina I. Schönherr +7 more
wiley +1 more source
Gut Epithelium of the Highly Toxic Ribbon Worm <i>Cephalothrix</i> cf. <i>simula</i> (Palaeonemertea, Nemertea) Contains Tetrodotoxin-Positive Bacterial Endosymbionts. [PDF]
Magarlamov TY, Malykin GV.
europepmc +1 more source
MOFs and COFs in Electronics: Bridging the Gap between Intrinsic Properties and Measured Performance
Metal‐organic frameworks (MOFs) and covalent organic frameworks (COFs) hold promise for advanced electronics. However, discrepancies in reported electrical conductivities highlight the importance of measurement methodologies. This review explores intrinsic charge transport mechanisms and extrinsic factors influencing performance, and critically ...
Jonas F. Pöhls, R. Thomas Weitz
wiley +1 more source
Influence of impeller configuration and operating parameters on granular mixing: a DEM investigation. [PDF]
Zhou ZH +9 more
europepmc +1 more source
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
wiley +1 more source
Research on the mechanical behavior of compression molding for simulated polymer-bonded explosives. [PDF]
Wu X, Tao J, Wang B, Ren H, Wang Y.
europepmc +1 more source
In Situ Study of Resistive Switching in a Nitride‐Based Memristive Device
In situ TEM biasing experiment demonstrates the volatile I‐V characteristic of MIM lamella device. In situ STEM‐EELS Ti L2/L3 ratio maps provide direct evidence of the oxygen vacancies migrations under positive/negative electrical bias, which is critical for revealing the RS mechanism for the MIM lamella device.
Di Zhang +19 more
wiley +1 more source

