AIMC Topic: Metals

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Artificial Adaptive and Maladaptive Sensory Receptors Based on a Surface-Dominated Diffusive Memristor.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
A biological receptor serves as sensory transduction from an external stimulus to an electrical signal. It allows humans to better match the environment by filtering out repetitive innocuous information and recognize potentially damaging stimuli thro...

A Liquid Metal Artificial Muscle.

Advanced materials (Deerfield Beach, Fla.)
Artificial muscles possess a vast potential in accelerating the development of robotics, exoskeletons, and prosthetics. Although a variety of emerging actuator technologies are reported, they suffer from several issues, such as high driving voltages,...

Stimulus-driven liquid metal and liquid crystal network actuators for programmable soft robotics.

Materials horizons
Sophisticated soft matter engineering has been endorsed as an emerging paradigm for developing untethered soft robots with built-in electronic functions and biomimetic adaptation capacities. However, the integration of flexible electronic components ...

Prediction Model of Hot Metal Silicon Content Based on Improved GA-BPNN.

Computational intelligence and neuroscience
The inconsistency of the detection period of blast furnace data and the large time delay of key parameters make the prediction of the hot metal silicon content face huge challenges. Aiming at the problem that the hot metal silicon content is not cons...

Human reliability analysis of high-temperature molten metal operation based on fuzzy CREAM and Bayesian network.

PloS one
Human errors are considered to be the main causation factors of high-temperature molten metal accidents in metallurgical enterprises. The complex working environment of high- temperature molten metal in metallurgical enterprises has an important infl...

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning.

Chemical reviews
Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior of the metal-organic bond, while very tunable for achieving target properties, is challenging to predict and necessitates searching a...

Machine learning differentiates enzymatic and non-enzymatic metals in proteins.

Nature communications
Metalloenzymes are 40% of all enzymes and can perform all seven classes of enzyme reactions. Because of the physicochemical similarities between the active sites of metalloenzymes and inactive metal binding sites, it is challenging to differentiate b...

Research on the process of small sample non-ferrous metal recognition and separation based on deep learning.

Waste management (New York, N.Y.)
Consumption of copper and aluminum has increased significantly in recent years; therefore, recycling these elements from the end-of-life vehicles (ELVs) will be of great economic value and social benefit. However, the separation of non-ferrous materi...

A robust identification method for nonferrous metal scraps based on deep learning and superpixel optimization.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA
End-of-life vehicles (ELVs) provide a particularly potent source of supply for metals. Hence, the recycling and sorting techniques for ferrous and nonferrous metal scraps from ELVs significantly increase metal resource utilization. However, different...

Novel Method Based on Hollow Laser Trapping-LIBS-Machine Learning for Simultaneous Quantitative Analysis of Multiple Metal Elements in a Single Microsized Particle in Air.

Analytical chemistry
Elemental identification of individual microsized aerosol particles is an important topic in air pollution studies. However, simultaneous and quantitative analysis of multiple constituents in a single aerosol particle with the noncontact in situ mann...