Results 131 to 140 of about 255,138 (288)

Cyclic Olefin Copolymers as Versatile Materials for Advanced Engineering Applications

Advanced Functional Materials, EarlyView.
Cyclic olefin copolymers (COCs) are presented as highly versatile materials combining tunable synthesis, excellent optical properties, and mechanical robustness. Their potential spans microfluidics, bioengineering, and advanced electronics, while emerging self‐healing and sustainable solutions highlight future opportunities.
Giulia Fredi, Norma Mallegni, Andrea Dorigato, Mario Milazzo   +3 more
wiley   +1 more source

Active Learning‐Accelerated Discovery of Fibrous Hydrogels with Tissue‐Mimetic Viscoelasticity

Advanced Functional Materials, EarlyView.
Active learning accelerates the design of fibrous hydrogels that mimic the viscoelasticity of native tissues. By integrating multi‐objective optimization and closed‐loop experimentation, this approach efficiently identifies optimal formulations from thousands of possibilities and decouples elasticity and viscosity. The resulting hydrogels offer tunable
Zhengkun Chen, Zheyuan Miao, Gary Tom, Austin H. Cheng, Elizaveta Gusarova, Benjamen Zondag, Tianyi Wu, Dianoosh Kalhori, Boris Hinz, Milad Abolhasani, Alán Aspuru‐Guzik, Eugenia Kumacheva   +11 more
wiley   +1 more source

Self‐Healing and Stretchable Synaptic Transistor

Advanced Functional Materials, EarlyView.
A self‐healing stretchable synaptic transistor (3S‐T) is realized using a p‐PVDF‐HFP‐DBP/PDMS‐MPU‐IU bilayer as gate insulator, where dipole‐dipole interaction enhances polarization to achieve a large memory window. Leveraging its neuronal biomimicry, the synaptic transistor demonstrates electrically compatibility with the biological brain. Furthermore,
Hyongsuk Choo, Sangyong Park, Yoonyi Jeong, Jaepyo Jang, Duhwan Seong, In Soo Kim, Myung‐Gil Kim, Mikyung Shin, Minah Suh, Donghee Son, Jin‐Hong Park   +10 more
wiley   +1 more source

Probabilistic modeling of decryption-failure bounds in CRYSTALS-Kyber under the post-quantum threat model

Machine Learning: Science and Technology
We present a probabilistic modeling framework for quantifying decryption-failure probability (DFP) in CRYSTALS–Kyber , the lattice-based key encapsulation mechanism standardized by National Institute of Standards and Technology as module-lattice-based ...
Cemil Emre Yavas, Lei Chen, Christopher Kadlec   +2 more
doaj   +1 more source

Exploring an Alternative to mRNA Vaccine Cold Chain Storage: MRNA‐Lipid Nanoparticle Stability When Dried in a Polymer Matrix

Advanced Functional Materials, EarlyView.
The nanostructure, size, and function of mRNA‐loaded lipid nanoparticles are evaluated before drying, within polymer microneedles, and after rehydration. The results reveal the polymer and LNP loadings required to recover nanostructure and preserve the delivery performance in dry‐state formulations.
Brendan P. Dyett, Shahad K. Alsaiari, John L. Daristotle, Timothy A. Forster, Alicia Lau, Dhruv Varshney, Rania Afaneh, Andrew R. Hanna, Charles Sloane, David Mankus, Haitao Yu, Jiali Zhai, DongSoo Yun, Lisa H. Tostanoski, Abigail Lytton‐Jean, Dan H. Barouch, Charlotte E. Conn, Robert Langer, Ana Jaklenec, Calum J. Drummond   +19 more
wiley   +1 more source

Modification of the post-quantum key encapsulation mechanism “Zemlyanika”

Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics
The cryptographic scheme of the key encapsulation mechanism (KEM) Zemlyanika (“Strawberry”), based on the problem of modular learning with errors (Module-Learning With Errors, Module-LWE), uses a reduction module in the form of a power of two, which provides highly efficient modular arithmetic, but excludes the use of number-theoretic transformations ...
Nadezhda Valerievna Daviduk, Feliks Zagidinovich Efendiev, Georgiy Aleksandrovich Popov   +2 more
openaire   +1 more source

Machine Learning‐Assisted Inverse Design of Soft and Multifunctional Hybrid Liquid Metal Composites

Advanced Functional Materials, EarlyView.
A machine learning framework is presented for inverse design of synthesizable multifunctional composites containing both liquid metal and solid inclusions. By integrating physics‐based modeling, data‐driven prediction, and Bayesian optimization, the approach enables intelligent design of experiments to identify optimal compositions and realize these ...
Lijun Zhou, Yunsik Ohm, Ren‐Mian Chin, Olivia Kerr, Krithika Manohar, Mohammad H. Malakooti   +5 more
wiley   +1 more source

NIST’s Module-Lattice-Based Key-Encapsulation Mechanism and its security

This thesis is an in-depth introduction to NIST's post-quantum Module-Lattice-Based Key-Encapsulation Mechanism (ML-KEM). We begin by introducing the problem at the core of ML-KEM, known as the Module Learning With Errors (MLWE) problem. In this problem,
Jokiniemi, Anna
core  

Dual‐Mode Magnetic Elastomer for On‐Demand Motion and Degradation

Advanced Functional Materials, EarlyView.
A dual‐mode magnetic elastomer is introduced, enabling DC field‐driven programmable actuation and AC field‐driven magnetothermal degradation. GHz‐range magnetic fields generate ultrafast heating of magnetic nanoparticles that activates cleavage of the silicone elastomer matrix.
Jieun Han, Hyun An, Min‐Ha Oh, Yu‐Lim Lee, Woojin Jeon, Kyung‐Sub Kim, Jae‐Hwan Lee, Seungmin Kang, Yoon‐Nam Kim, Ji‐Won Kang, Daewhan Kim, Min Sang Kwon, Sang‐Koog Kim, Seung‐Kyun Kang   +13 more
wiley   +1 more source

A Key Encapsulation Mechanism from Low Density Lattice Codes

CoRR
Key Encapsulation Mechanisms (KEMs) are a set of cryptographic techniques that are designed to provide symmetric encryption key using asymmetric mechanism (public key). In the current study, we concentrate on design and analysis of key encapsulation mechanism from low density lattice codes (KEM-LDLC) to go down the key size by keeping an acceptable ...
openaire   +2 more sources