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Nicola H. Green

dblp:304/4551 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2021
0000-0001-5413-0642ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Robot manipulation · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
tissue engineering
0.112021
Design and Development of a Robotic Bioreactor for In Vitro Tissue Engineering · ICRA 2021

Methods — techniques the papers use, named apart from their topics

stiffness measurement · 1.0closed-loop force control · 1.0
YearPublicationVenuePosition
2021 Design and Development of a Robotic Bioreactor for In Vitro Tissue Engineering
abstract
In this study, a novel robotic bioreactor is presented with capabilities of closed-loop control of force and displacement applied to a tissue scaffold and tissue scaffold stiffness calculation. These characteristics bring the potential of a robotic bioreactor that can optimize the mechanical properties of tissue constructs in order for them to match those of native tissues. Custom position and force control signals are designed to maintain a steady tensioning of the tissue scaffold while the latter one’s mechanical properties evolve in time. We propose a simple model to support the hypothesis that the stiffness of a cell-seeded scaffold increases over time, and thus force control signals need to be adjusted accordingly. The robotic bioreactor is able to measure the stiffness of a scaffold sample relatively accurately, with an average standard deviation of 0.2N/mm. The combination of accurate stiffness measurements and a closed-loop control system equips the robotic bioreactor with the fundamental requirements to achieve stiffness based force control in future in vitro experiments, and thus to a tissue-scaffold responsive technology for advanced tissue engineering.
Abigail F. Smith, Jeerawan Thanarak, Marco Pontin, Nicola H. Green, Dana D. Damian
ICRA4