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Laia Mogas-Soldevila

Researcher at Tufts University

Publications -  16
Citations -  297

Laia Mogas-Soldevila is an academic researcher from Tufts University. The author has contributed to research in topics: Workflow & Modular design. The author has an hindex of 8, co-authored 13 publications receiving 207 citations. Previous affiliations of Laia Mogas-Soldevila include Massachusetts Institute of Technology.

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Water-Based Robotic Fabrication: Large-Scale Additive Manufacturing of Functionally Graded Hydrogel Composites via Multichamber Extrusion

TL;DR: In this article, a water-based robotic fabrication approach and enabling technology for additive manufacturing of biodegradable hydrogel composites is presented, which focuses on the combination of expanding the dimensions of the fabrication envelope, developing structural materials for additive deposition, incorporating material-property gradients, and manufacturing architectural-scale biomaterials.
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MetaMesh: A hierarchical computational model for design and fabrication of biomimetic armored surfaces

TL;DR: MetaMesh as mentioned in this paper adapts a segmented fish scale exoskeleton to fit complex "host surfaces" on top of which the scale units are computed, and operates in three levels of resolution: (i) locally, to construct unit geometries based on shape parameters of scales.
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Flow-based fabrication

TL;DR: A seamless computational workflow for the design and direct digital fabrication of multi-material and multi-scale structured objects that encodes for and integrates domain-specific meta-data relating to local, regional and global feature resolution of heterogeneous material organizations.
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Large‐Scale Patterning of Reactive Surfaces for Wearable and Environmentally Deployable Sensors

TL;DR: A complementary approach is presented to wearable sensing by using a large-scale, conformal, distributed format that relies on the use of biomaterial-based inks to print and stabilize deterministic patterns of biochemical reporters with high resolution.

MetaMesh: A hierarchical computational model for design and fabrication of biomimetic armored surfaces

TL;DR: This research presents a novel mesoporous composite material for Soldier Nanotechnologies that has the potential to significantly reduce the risk of infectious disease and injury to soldiers.