Biology subjects

Bennett, D. T.

Publications and source records attributed to Bennett, D. T..

3 recordsLinked to original sources

bioRxiv2026

3D bioprinting of engineered living materials in support slurries for complex free-standing structures

The combination of synthetic biology and additive manufacturing has driven major changes in production of biomaterials, especially through the use of three-dimensional (3D) bioprinting to create engineered living materials. However, current fabrication methods can be limited by prohibitive hardware costs and the inability to maintain structural fidelity in complex, free-form living architectures. This work demonstrates how to build a low-cost, open-source 3D bioprinting platform that can make complicated bacterial structures with complex geometry and high dimensional accuracy. A commercially available, conventional fused deposition modeling 3D printer was modified to create a bioprinting system that is simple to build. The modified bioprinter, which costs around $450, is less expensive than many commercial bioprinters. This 3D-printing technology uses slurry-based support bath methods featuring low-cost gelatin and agarose microparticles, resulting in structures with a high aspect ratio (>8:1) and feature sizes as small as 260 m. The optimization of critical printing settings, including the ability of the bioink to retract during non-print movements, resulted in a reduction of unwanted bacterial deposition by nearly two orders of magnitude. Long-term viability experiments showed that bacteria in the bioprints could survive for at least 28 days with nutrient supplementation. Additionally, 3D-printed engineered biofilms revealed that incubation conditions and extracellular matrix composition significantly impacted the mechanical properties of printed constructs, with tradeoffs between matrix production and mechanical integrity. This study showcases an accessible 3D bioprinting platform for advanced bioprinting technologies, enabling development of engineered living materials with potential applications in synthetic biology, biotechnology, and tissue engineering.

synthetic biology↗

bioRxiv2025

Transforming dairy waste into hydrogen fuel using alginate-encapsulated bacterial co-cultures

Dairy waste, such as whey resulting from cheese production, is produced in massive volumes worldwide and is regarded as environmentally difficult to dispose of due to its high organic content1. Harnessing the potential of this waste material to support the synthesis of valuable products such as hydrogen fuel or reduced graphene oxide, which may be utilized for conductive thin films and energy storage2,3, can reduce waste and add revenue streams for dairy farmers. Here, we demonstrate a circular bioeconomy using alginate-encapsulated co-cultures of Shewanella oneidensis together with lactic-acid-producing bacteria Klebsiella pneumoniae. These co-cultures can directly metabolize unprocessed cheese-making waste as an electron source instead of costly, environmentally high-impact lactic acid4,5. Alginate-encapsulated co-cultures fed unprocessed dairy waste showed a 2-to-3-fold higher graphene oxide reduction rate compared to S. oneidensis monocultures with no supplemental electron source. Encapsulated co-cultures were able to be recycled for more than 30 days with no measurable decrease in graphene oxide reduction efficiency, showing compatibility with future industrial scaling. Photocatalytic hydrogen generation with cadmium selenide quantum dots as the catalyst resulted in 6-fold increases in hydrogen produced by co-cultures using milk as an electron source precursor for the system in comparison to S. oneidensis monocultures without any added electron sources. Thus, dairy waste may be processed to drive the synthesis of valuable products utilizing microbial electron transfer processes, converting a significant fluvial environmental pollutant into a valuable renewable energy resource that could provide a robust alternative revenue stream for dairy farmers in a volatile industry.

microbiology↗

bioRxiv2024

Robust measurement of microbial reduction of graphene oxide nanoparticles using image analysis

Shewanella oneidensis (S. oneidensis) has the capacity to reduce electron acceptors within a medium and is thus used frequently in microbial fuel generation, pollutant breakdown, and nanoparticle fabrication. Microbial fuel setups, however, often require costly or labour-intensive components, thus making optimization of their performance onerous. For rapid optimization of setup conditions, a model reduction assay can be employed to allow simultaneous, large-scale experiments at lower cost and effort. Since S. oneidensis uses different extracellular electron transfer pathways depending on the electron acceptor, it is essential to use a reduction assay that mirrors the pathways employed in the microbial fuel system. For microbial fuel setups that use nanoparticles to stimulate electron transfer, reduction of graphene oxide provides a more accurate model than other commonly used assays as it is a bulk material that forms flocculates in solutions with a large ionic component. However, graphene oxide flocculates can interfere with traditional absorbance-based measurement techniques. This study introduces a novel image analysis method for quantifying graphene oxide reduction, showing improved performance and statistical accuracy over traditional methods. A comparative analysis shows that the image analysis method produces smaller errors between replicates and reveals more statistically significant differences between samples than traditional plate reader measurements under conditions causing graphene oxide flocculation. Image analysis can also detect reduction activity at earlier time points due to its utilization of larger solution volumes, enhancing color detection. These improvements in accuracy make image analysis a promising method for optimizing microbial fuel cells that use nanoparticles or bulk substrates. IMPORTANCEShewanella oneidensis (S. oneidensis) is widely used in reduction processes such as microbial fuel generation due to its capacity to reduce electron acceptors. Often, these setups are labor intensive to operate and require days to produce results, so use of a model assay would reduce the time and expense needed for optimization. Our research developed a novel digital analysis method for analysis of graphene oxide flocculates that may be utilized as a model assay for reduction platforms featuring nanoparticles. Use of this model reduction assay will enable rapid optimization and drive improvements in the microbial fuel generation sector.

bioengineering↗