Results 81 to 90 of about 5,328 (242)

An Avalanche Effect Nanomodulator Triggers Cascade Ferroptosis/Cuproptosis for Immunosuppressive Microenvironment Remodeling

Advanced Science, EarlyView.
A nanomodulator called L‐Arginine/lificiguat@Copper‐hollow Prussian blue@Calcium phosphate nanoparticles is constructed to launch a self‐amplifying “avalanche effect” in tumor cells. The ensuing cascade, triggered by the acidic tumor microenvironment and near‐infrared light, induces calcium overload, generates a reactive oxygen species and reactive ...
Hongmei Zhou, Caixi Yu, Guilan Xu, Lijuan Liu, Yujia Liang, Yulin Liang, He Ding, Litu Zhang, Piaoping Yang, Chen Wang   +9 more
wiley   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Heavy Metal Contamination: Impacts From Plants to the Food Chain and Eco‐Friendly Amelioration Strategies

AgriFood: Journal of Agricultural Products for Food, EarlyView.
Heavy metal pollution reduces soil health and threatens food safety by entering plants; however, the use of nanoparticles, biofertilizers, and organic fertilizers such as biochar offers a promising strategy for improvement and sustainable agriculture. ABSTRACT Heavy metal (HM) pollution, resulting from human activities such as mining and the excessive ...
Sina Siavash Moghaddam, Latifeh Pourakbar, Sevda Alizadeh, Zahra Alizadeh, Jelena Popović‐Djordjević   +4 more
wiley   +1 more source

A Unifying Approach to Self‐Organizing Systems Interacting via Conservation Laws

Advanced Intelligent Discovery, EarlyView.
The article develops a unified way to model and analyze self‐organizing systems whose interactions are constrained by conservation laws. It represents physical/biological/engineered networks as graphs and builds projection operators (from incidence/cycle structure) that enforce those constraints and decompose network variables into constrained versus ...
F. Barrows, G. Zhang, S. Anand, Z. Chen, J. Lin, A. Desai, S. Martiniani, F. Caravelli   +7 more
wiley   +1 more source

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Advanced Intelligent Discovery, EarlyView.
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 more
wiley   +1 more source

RRNS Arith Lib—Open-Source Redundant Residue Number System Arithmetics for Reliable Circuits

IEEE Open Journal of Circuits and Systems
A Residue Number System (RNS) represents integers through a set of residues obtained by integer division using a predefined set of pairwise coprime moduli. RNS is well-known for enabling efficient carry-free arithmetic and representing large numbers with
Tim Oberschulte, Enno Sievers, Holger Blume   +2 more
doaj   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

A Hybrid Forward/Reverse Converter in Reversible Logic to Reduce Hardware Complexity of Residual Number System

Majlesi Journal of Electrical Engineering
As an emerging technology, reversible computing enables the development of high-performance computing systems with low energy consumption. A residue number system (RNS) that performs arithmetic operations in parallel with error tolerance and no carry ...
Ailin Asadpour, Amir Sabbagh, Azadeh Emrani   +2 more
doaj   +1 more source

Machine‐Learning‐Assisted Onset‐Time Determination in Transient Luminescence Thermometry

Advanced Intelligent Discovery, EarlyView.
Artificial neural networks enable autonomous extraction of onset times from transient heating curves in luminescence thermometry. Using Ln3+‐doped upconverting nanoparticles as luminescent thermometers, we combine experimental transients with physically motivated synthetic curves to enhance data diversity and improve generalization.
David J. Sousa, Erving Ximendes, Luís D. Carlos, Carlos D. S. Brites   +3 more
wiley   +1 more source

Hydration Behavior of Tricalcium Silicate in Seawater Relevant Salt Systems: A Hybrid Study with the Aid of Machine Learning

Advanced Intelligent Discovery, EarlyView.
The hydration behavior of C3S in seawater‐relevant solutions is studied based on experiments, boundary nucleation and growth (BNG) modeling, and machine learning. The main ions included in seawater modify hydration mechanisms, with MgCl2 showing the strongest acceleration effect at the same concentration.
Yanjie Sun, Chen Zhang, Ting Zhang, Mengze Hong, Peiliang Shen, Chi Sun Poon, Gang Li   +6 more
wiley   +1 more source