Digital Mechanical Metamaterial: Encoding Mechanical Information with Graphical Stiffness Pattern for Adaptive Soft Machines.

Inspired by the adaptive features exhibited by biological organisms like the octopus, soft machines that can tune their shape and mechanical properties have shown great potential in applications involving unstructured and continuously changing environments. However, current soft machines are far from achieving the same level of adaptability as their biological counterparts, hampered by limited real-time tunability and severely deficient reprogrammable space of properties and functionalities. As a steppingstone toward fully adaptive soft robots and smart interactive machines, an encodable multifunctional material that uses graphical stiffness patterns is introduced here to in situ program versatile mechanical capabilities without requiring additional infrastructure. Through independently switching the digital binary stiffness states (soft or rigid) of individual constituent units of a simple auxetic structure with elliptical voids, in situ and gradational tunability is demonstrated here in various mechanical qualities such as shape-shifting and -memory, stress-strain response, and Poisson's ratio under compressive load as well as application-oriented functionalities such as tunable and reusable energy absorption and pressure delivery. This digitally programmable material is expected to pave the way toward multienvironment soft robots and interactive machines.

AI-enabled biosensing for rapid pathogen detection: From liquid food to agricultural water.

Rapid pathogen detection in food and agricultural water is essential for ensuring food safety and public health. However, complex and noisy environmental background matrices delay the identification of pathogens and require highly trained personnel. Here, we present an AI-biosensing framework for accelerated and automated pathogen detection in various water samples, from liquid food to agricultural water. A deep learning model was used to identify and quantify target bacteria based on their microscopic patterns generated by specific interactions with bacteriophages. The model was trained on augmented datasets to maximize data efficiency, using input images of selected bacterial species, and then fine-tuned on a mixed culture. Model inference was performed on real-world water samples containing environmental noises unseen during model training. Overall, our AI model trained solely on lab-cultured bacteria achieved rapid (< 5.5 h) prediction with 80-100% accuracy on the real-world water samples, demonstrating its ability to generalize to unseen data. Our study highlights the potential applications in microbial water quality monitoring during food and agricultural processes.

Accelerating the Detection of Bacteria in Food Using Artificial Intelligence and Optical Imaging.

In assessing food microbial safety, the presence of Escherichia coli is a critical indicator of fecal contamination. However, conventional detection methods require the isolation of bacterial macrocolonies for biochemical or genetic characterization, which takes a few days and is labor-intensive. In this study, we show that the real-time object detection and classification algorithm You Only Look Once version 4 (YOLOv4) can accurately identify the presence of E. coli at the microcolony stage after a 3-h cultivation. Integrating with phase-contrast microscopic imaging, YOLOv4 discriminated E. coli from seven other common foodborne bacterial species with an average precision of 94%. This approach also enabled the rapid quantification of E. coli concentrations over 3 orders of magnitude with an R2 of 0.995. For romaine lettuce spiked with E. coli (10 to 103 CFU/g), the trained YOLOv4 detector had a false-negative rate of less than 10%. This approach accelerates analysis and avoids manual result determination, which has the potential to be applied as a rapid and user-friendly bacterial sensing approach in food industries. IMPORTANCE A simple, cost-effective, and rapid method is desired to identify potential pathogen contamination in food products and thus prevent foodborne illnesses and outbreaks. This study combined artificial intelligence (AI) and optical imaging to detect bacteria at the microcolony stage within 3 h of inoculation. This approach eliminates the need for time-consuming culture-based colony isolation and resource-intensive molecular approaches for bacterial identification. The approach developed in this study is broadly applicable for the identification of diverse bacterial species. In addition, this approach can be implemented in resource-limited areas, as it does not require expensive instruments and significantly trained human resources. This AI-assisted detection not only achieves high accuracy in bacterial classification but also provides the potential for automated bacterial detection, reducing labor workloads in food industries, environmental monitoring, and clinical settings.

Modeling bioaffinity-based targeted delivery of antimicrobials to Escherichia coli biofilms using yeast microparticles. Part II: Parameter evaluation and validation.

The design of bioaffinity-based targeted delivery systems for biofilm inactivation may require a comprehensive understanding of physicochemical and biochemical properties of biobased antimicrobial particles and their interactions with biofilm. In this study, Escherichia coli biofilm inactivation by chlorine-charged yeast microparticles was numerically simulated, and the roles of chemical stability, binding affinity, and controlled release of this targeted delivery system were assessed using this numerical simulation. The simulation results were experimentally validated using two different types of yeast microparticles. The results of this study illustrate that chorine stability achieved by yeast microparticles was a key factor for improved biofilm inactivation in an organic-rich environment (>6 additional log reduction in 20 min compared to the free chlorine treatment). Moreover, the binding affinity of yeast microparticles to E. coli biofilms was another key factor for an enhanced inactivation of biofilm, as a 10-fold increase in binding rate resulted in a 4.2-fold faster inactivation. Overall, the mechanistic modeling framework developed in this study could guide the design and development of biobased particles for targeted inactivation of biofilms.

Modeling bioaffinity-based targeted delivery of antimicrobials to Escherichia coli biofilms using yeast microparticles. Part I: Model development and numerical simulation.

Biofilms are potential reservoirs for pathogenic microbes leading to a significant challenge for food safety, ecosystems, and human health. Various micro-and nanoparticles have been experimentally evaluated to improve biofilm inactivation by targeted delivery of antimicrobials. However, the role of transport processes and reaction kinetics of these delivery systems are not well understood. In this study, a mechanistic modeling approach was developed to understand the targeted delivery of chlorine to an Escherichia coli biofilm using a novel bioaffinity-based yeast microparticle. Biofilm inactivation by this delivery system was numerically simulated as a combination of reaction kinetics and transport phenomena. Simulation results demonstrate that the targeted delivery system achieved 7 log reduction within 16.2 min, while the equivalent level of conventional free chlorine achieved only 3.6 log reduction for the same treatment time. These numerical results matched the experimental observations in our previous study. This study illustrates the potential of a mechanistic modeling approach to improve fundamental understanding and guide the design of targeted inactivation of biofilms using biobased particles.

Chlorine Rechargeable Biocidal N-Halamine Nanofibrous Membranes Incorporated with Bifunctional Zwitterionic Polymers for Efficient Water Disinfection Applications.

An intrinsically hydrophilic nanofibrous membrane with chlorine rechargeable biocidal and antifouling functions was prepared by using a combination of chemically bonded N-halamine moieties and zwitterionic polymers (PEI-S). The designed nanofibrous membrane, named as PEI-S@BNF-2 h, can exhibit integrated features of reduced bacterial adhesion, rechargeable biocidal activity, and easy release of killed bacteria by using mild hydrodynamic forces. The representative functional performances of the PEI-S@BNF-2 h membrane include high active chlorine capacity (>4000 ppm), large specific surface area, ease of chlorine rechargeability, long-term stability, and exceptional biocidal activity (99.9999% via contact killing). More importantly, the zwitterionic polymer moieties (PEI-S) brought robust antifouling properties to this biocidal membrane, therefore reducing the biofouling-biofilm effect and prolonging the lifetime of the filtration membrane. These attributes enable the PEI-S@BNF-2 h nanofibrous membrane to effectively disinfect the microbe-contaminated water with high fluxes (10,000 L m-2 h-1) and maintain itself clean for a long-term application.