We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
From biodiversity to design: a phylogeny-aware, data-driven framework for biological model selection in bio-inspired design
AI summary Read the abstract
Scientists are building better tools to design technology inspired by nature—like filters that mimic how animals such as clams or barnacles naturally strain particles from water. This new framework helps engineers systematically pick the best animal or organism to copy from, using a scoring system based on available research, uniqueness, and evolutionary traits, rather than relying on guesswork. The researchers tested it specifically for designing microplastic filters, which could eventually help reduce the tiny plastic particles found in our water and food supply.
Selecting suitable biological models remains one of the most challenging and least formalised steps in biomimetic design. Existing tools support searching for biological strategies and transferring principles into engineering concepts, but provide limited guidance on which organism to prioritise once multiple plausible candidates are identified. Here we present a phylogeny-informed, data-driven framework that structures model selection as an explicit comparison. The framework computes four module scores per candidate model: data sufficiency, innovativeness, phylogenetic characteristics, and an open-ended contextual module for project-specific constraints. Users assign weights to the modules to reflect resources, timelines, and design requirements, yielding an overall compatible score and a ranked shortlist. We demonstrate the framework in a microplastic-filtration case study using 35 suspension-feeding taxa and three archetypal user scenarios. Rankings reveal a compact set of candidates that remains competitive across scenarios, while a smaller subset shifts in response to changes in priorities, thereby distinguishing robust starting points from context-dependent opportunities. Overall, the framework fills an under-supported gap in current biomimetic workflows by providing a structured decision-support layer for biological model selection.
More Papers Like This
From biodiversity to design: a phylogeny-aware, data-driven framework for biological model selection in bio-inspired design
AI summary Read the abstract
Scientists have created a smart tool that helps engineers pick the best animal or organism to copy when designing new technology—like filters that remove harmful microplastics from water. As a case study, they tested it on 35 filter-feeding sea creatures (like clams and sponges) to find which ones offer the best blueprints for microplastic-catching devices, which could eventually help reduce our exposure to these tiny pollutants in drinking water and food. This is an early-stage design framework, not a finished filtration product, but it could speed up the development of nature-inspired solutions to the microplastics problem.
Biologically inspired solutions to micro- and nanoplastic filtration in wastewater treatment plants
AI summary Read the abstract
Tiny plastic particles (microplastics and even smaller nanoplastics) are slipping through our wastewater treatment plants and ending up in rivers, oceans, and eventually our food—yet current filters just aren't built to catch them. Researchers looked to nature for solutions, studying how living organisms (like filter-feeding animals) trap tiny particles, and identified promising biological designs that could inspire better, more effective filtration technology. This is an early-stage conceptual study, not a ready-to-use product, but it lays groundwork for future filters that could reduce the amount of plastic pollution making its way into our water and bod
Towards a More Sustainable Water Treatment: Design of a Hydrodynamic Test Rig and Testing of a Novel Microplastic Filter Using Biomimetics
AI summary Read the abstract
Researchers designed a hydrodynamic test rig and a novel biomimetic microplastic filter inspired by aquatic filter-feeding organisms, aiming to improve solid-liquid separation in water treatment. The study demonstrates how biological filtration strategies can inform more sustainable industrial microplastic removal approaches.
From Model Proliferation to Deployment Intelligence: A Risk-Traceable System-Aware Model Selection Framework
AI summary Read the abstract
Researchers tested a new framework for choosing the best computer model for different engineering tasks, including microplastics detection. Across several test scenarios, the method sometimes picked a different model than standard approaches, showing that simple rankings do not always reflect how reliable a model choice is in practice.
The Natural Robotics Contest: Crowdsourced Biomimetic Design
AI summary Read the abstract
This paper describes the Natural Robotics Contest, a crowdsourced biomimetic design competition that allows the public to submit nature-inspired engineering concepts that are then fabricated into real systems. The winning design in the featured contest was a robotic fish using gill-inspired filter structures to collect microplastics from water, fabricated as an open-source robot with a novel 3D-printed gill mechanism. The authors discuss how public submissions reveal perceptions of pressing engineering challenges and the value of nature-inspired design for environmental problems including microplastic pollution.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.