Results 191 to 200 of about 802,912 (335)

Test Time Reduction Reusing Multiple Processors in a Network-on-Chip Based Architecture

, 2007
Alexandre M. Amory, Marcelo Lubaszewski, Fernando Moraes, Edson Moreno   +3 more
openalex   +1 more source

MOFs and COFs in Electronics: Bridging the Gap between Intrinsic Properties and Measured Performance

Advanced Functional Materials, EarlyView.
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

Efficient Catalysis by Sodium Hypophosphite for Solid-State Polymerization of High-Viscosity PA6/66 Copolyamide: Kinetics, Process Optimization, and Industrial Application. [PDF]

Polymers (Basel)
Jiang F, Yu C, Hu Z, Wu Y, Li X.
europepmc   +1 more source

Boiling Refrigerant Type Compact Cooling Unit for Computer Chip. Reduction of Thermal Resistance at Boiling.

, 2001
Eitaro TANAKA, Kiyoshi Kawaguchi, Tadayoshi Terao, Masahiko Suzuki, Hiroshi Tanaka   +4 more
openalex   +2 more sources

A 0.13 μm CMOS System-on-Chip for a 512 × 424 Time-of-Flight Image Sensor With Multi-Frequency Photo-Demodulation up to 130 MHz and 2 GS/s ADC [PDF]

, 2014
Cyrus Bamji, Patrick A. O’Connor, Tamer A. Elkhatib, Swati Mehta, Barry Thompson, Lawrence A. Prather, Dane Snow, Onur Can Akkaya, Andy Daniel, Andrew D. Payne, Travis Perry, Mike Fenton, V. W. S. Chan   +12 more
openalex   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
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, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Modeling neurovascular dysfunction in Alzheimer's disease using an isogenic brain-chip model. [PDF]

Fluids Barriers CNS
Shen AN, Matazel KS, Gill WD, Ewart L, Daughters RS, Rosas-Hernandez H.   +5 more
europepmc   +1 more source

SANS-on-a-chip: in-situ monitoring of micro-emulsification process and flow-induced structural change in microfluidics

Dr Peixun Li, Dr Lauren Matthews, Professor Yujun Feng, Dr James Doutch, Dr Najet Mahmoudi, Mr Moyi Li, Dr Steve King, Dr Xuezhi Zhao   +7 more
openalex   +1 more source

Asymmetrically and Vertically Coupled Hybrid Si/GaN Microring Resonators for On-Chip Optical Interconnects [PDF]

, 2015
Borriboon Thubthimthong, T. Sasaki, Kazuhiro Hane   +2 more
openalex   +1 more source

Expanding Chemical Space of Nucleic Acid Nanoparticles for Tunable Antiviral‐Like Immunomodulatory Responses and Potent Adjuvant Activity

Advanced Functional Materials, EarlyView.
We introduce a nucleic acid nanoparticle (NANP) platform designed to be rrecognized by the human innate immune system in a regulated manner. By changing chemical composition while maintaining constant architectural parameters, we identify key determinants of immunorecognition enabling the rational design of NANPs with tunable immune activation profiles
Martin Panigaj, Hannah S. Newton, Jian Wang, Laxmi Pandey, Yelixza I. Avilla, Phong Nguyen, Morgan Chandler, Justin Halman, Yasmine Radwan, Stephanie Smith, Elijah Edmondson, Simone Difilippantonio, Chelsea Sanders, Bao Tran, Yongmei Zhao, Shaojun Xie, Edward Cedrone, Barry W. Neun, Meni Wanunu, Nikolay V. Dokholyan, Marina A. Dobrovolskaia, Kirill A. Afonin   +21 more
wiley   +1 more source