AIMC Topic: Polymers

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An Interdisciplinary Tutorial: A Self-Healing Soft Finger with Embedded Sensor.

Sensors (Basel, Switzerland)
In the field of soft robotics, knowledge of material science is becoming more and more important. However, many researchers have a background in only one of both domains. To aid the understanding of the other domain, this tutorial describes the compl...

Dip-coating electromechanically active polymer actuators with SIBS from midblock-selective solvents to achieve full encapsulation for biomedical applications.

Scientific reports
Soft and compliant ionic electromechanically active polymer actuators (IEAPs) are a promising class of smart materials for biomedical and soft robotics applications. These materials change their shape in response to external stimuli like the electric...

Deep Learning-Enhanced Potentiometric Aptasensing with Magneto-Controlled Sensors.

Angewandte Chemie (International ed. in English)
Bioelectronic sensors that report charge changes of a biomolecule upon target binding enable direct and sensitive analyte detection but remain a major challenge for potentiometric measurement, mainly due to Debye Length limitations and the need for m...

Exploring Machine Learning-Based Fault Monitoring for Polymer-Based Additive Manufacturing: Challenges and Opportunities.

Sensors (Basel, Switzerland)
Three-dimensional printing, often known as additive manufacturing (AM), is a groundbreaking technique that enables rapid prototyping. Monitoring AM delivers benefits, as monitoring print quality can prevent waste and excess material costs. Machine le...

Unified machine learning protocol for copolymer structure-property predictions.

STAR protocols
Structure-property relationships are extremely valuable when predicting the properties of polymers. This protocol demonstrates a step-by-step approach, based on multiple machine learning (ML) architectures, which is capable of processing copolymer ty...

Equation learning to identify nano-engineered particle-cell interactions: an interpretable machine learning approach.

Nanoscale
Designing nano-engineered particles capable of the delivery of therapeutic and diagnostic agents to a specific target remains a significant challenge. Understanding how interactions between particles and cells are impacted by the physicochemical prop...

Emergence of MXene and MXene-Polymer Hybrid Membranes as Future- Environmental Remediation Strategies.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
The continuous deterioration of the environment due to extensive industrialization and urbanization has raised the requirement to devise high-performance environmental remediation technologies. Membrane technologies, primarily based on conventional p...

AI-Based Support System for Monitoring the Quality of a Product within Industry 4.0 Paradigm.

Sensors (Basel, Switzerland)
Three-dimensional (3D) printing, also known as additive manufacturing (AM), has already shown its potential in the fourth technological revolution (Industry 4.0), demonstrating remarkable applications in manufacturing, including of medical devices. T...

Functional Liquid Crystal Elastomers Based on Dynamic Covalent Chemistry.

Chemistry (Weinheim an der Bergstrasse, Germany)
The marriage of liquid crystal elastomers with dynamic covalent chemistry can be a new paradigm for the development of dynamic and intelligent polymers with versatile functionalities, which is of paramount significance for many emerging applications ...

Functional Output Regression for Machine Learning in Materials Science.

Journal of chemical information and modeling
In recent years, there has been a rapid growth in the use of machine learning in material science. Conventionally, a trained predictive model describes a scalar output variable, such as thermodynamic, electronic, or mechanical properties, as a functi...