Metabolism is a key process that makes life alive-the combination of anabolism and catabolism sustains life by a continuous flux of matter and energy. In other words, the materials comprising life are synthesized, assembled, dissipated, and decomposed autonomously in a controlled, hierarchical manner using biological processes. Although some biological approaches for creating dynamic materials have been reported, the construction of such materials by mimicking metabolism from scratch based on bioengineering has not yet been achieved. Various chemical approaches, especially dissipative assemblies, allow the construction of dynamic materials in a synthetic fashion, analogous to part of metabolism. Inspired by these approaches, here, we report a bottom-up construction of dynamic biomaterials powered by artificial metabolism, representing a combination of irreversible biosynthesis and dissipative assembly processes. An emergent locomotion behavior resembling a slime mold was programmed with this material by using an abstract design model similar to mechanical systems. Dynamic properties, such as autonomous pattern generation and continuous polarized regeneration, enabled locomotion along the designated tracks against a constant flow. Furthermore, an emergent racing behavior of two locomotive bodies was achieved by expanding the program. Other applications, including pathogen detection and hybrid nanomaterials, illustrated further potential use of this material. Dynamic biomaterials powered by artificial metabolism could provide a previously unexplored route to realize "artificial" biological systems with regenerating and self-sustaining characteristics.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1126/scirobotics.aaw3512 | DOI Listing |
Front Robot AI
December 2024
CREATE-Lab, Department of Mechanical Engineering, Swiss Federal Technology Institute of Lausanne (EPFL), Lausanne, Switzerland.
Creativity and style in music playing originates from constraints and imperfect interactions between instruments and players. Digital and robotic systems have so far been unable to capture this naturalistic playing. Whether as an additional tool for musicians, function restoration with prosthetics, or artificial intelligence-powered systems, the physical embodiment and interactions generated are critical for expression and connection with an audience.
View Article and Find Full Text PDFJ Yeungnam Med Sci
December 2024
Department of Internal Medicine, Yeungnam University College of Medicine, Daegu, Korea.
The coronavirus disease 2019 pandemic has underscored the limitations of traditional diagnostic methods, particularly in ensuring the safety of healthcare workers and patients during infectious outbreaks. Smartphone-based digital stethoscopes enhanced with artificial intelligence (AI) have emerged as potential tools for addressing these challenges by enabling remote, efficient, and accessible auscultation. Despite advancements, most existing systems depend on additional hardware and external processing, increasing costs and complicating deployment.
View Article and Find Full Text PDFEnviron Res
December 2024
INSTM and Chemistry for Technologies Laboratory, Department of Mechanical and Industrial Engineering, University of Brescia, via Branze 38, 25123, Brescia, Italy. Electronic address:
The integration of Artificial Intelligence (AI) into the discovery of new materials offers significant potential for advancing sustainable technologies. This paper presents a novel approach leveraging AI-driven methodologies to identify a new malate structure derived from the treatment of spent lithium-ion batteries. By analysing bibliographic data and incorporating domain-specific knowledge, AI facilitated the identification and structure refinement of a new malate complex containing different metals (Ni, Mn, Co, and Cu).
View Article and Find Full Text PDFSci Rep
December 2024
Department of Electrical and Electronics Engineering, SR University, Warangal, Telangana, 506371, India.
Autonomous microgrids (ATMG), with green power sources, like solar and wind, require an efficient control scheme to secure frequency stability. The weather and locationally dependent behavior of the green power sources impact the system frequency imperfectly. This paper develops an intelligent, i.
View Article and Find Full Text PDFSci Rep
December 2024
School of Public Administration, Guangzhou University, Guangzhou, 510006, China.
The randomness and volatility of existing clean energy sources have increased the complexity of grid scheduling. To address this issue, this work proposes an artificial intelligence (AI) empowered method based on the Environmental, Social, and Governance (ESG) big data platform, focusing on multi-objective scheduling optimization for clean energy. This work employs a combination of Particle Swarm Optimization (PSO) and Deep Q-Network (DQN) to enhance grid scheduling efficiency and clean energy utilization.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!