Fabric-like Electrospun PVAc-Graphene Nanofiber Webs as Wearable and Degradable Piezocapacitive Sensors.

Flexible piezocapacitive sensors utilizing nanomaterial-polymer composite-based nanofibrous membranes offer an attractive alternative to more traditional piezoelectric and piezoresistive wearable sensors owing to their ultralow powered nature, fast response, low hysteresis, and insensitivity to temperature change. In this work, we propose a facile method of fabricating electrospun graphene-dispersed PVAc nanofibrous membrane-based piezocapacitive sensors for applications in IoT-enabled wearables and human physiological function monitoring. A series of electrical and material characterization experiments were conducted on both the pristine and graphene-dispersed PVAc nanofibers to understand the effect of graphene addition on nanofiber morphology, dielectric response, and pressure sensing performance.

Clean and Safe Drinking Water Systems Metagenomics Data and Artificial Intelligence: State-of-the-Art and Future Perspective.

The use of next-generation sequencing technologies in drinking water distribution systems (DWDS) has shed insight into the microbial communities' composition, and interaction in the drinking water microbiome. For the past two decades, various studies have been conducted in which metagenomics data have been collected over extended periods and analyzed spatially and temporally to understand the dynamics of microbial communities in DWDS. In this literature review, we outline the findings which were reported in the literature on what kind of occupancy-abundance patterns are exhibited in the drinking water microbiome, how the drinking water microbiome dynamically evolves spatially and temporally in the distribution networks, how different microbial communities co-exist, and what kind of clusters exist in the drinking water ecosystem.

Biomimetic Soft Polymer Microstructures and Piezoresistive Graphene MEMS Sensors Using Sacrificial Metal 3D Printing.

Recent advances in 3D printing technology have enabled unprecedented design freedom across an ever-expanding portfolio of materials. However, direct 3D printing of soft polymeric materials such as polydimethylsiloxane (PDMS) is challenging, especially for structural complexities such as high-aspect ratio (>20) structures, 3D microfluidic channels (∼150 μm diameter), and biomimetic microstructures. This work presents a novel processing method entailing 3D printing of a thin-walled sacrificial metallic mold, soft polymer casting, and acidic etching of the mold.

Bioinspired PDMS-graphene cantilever flow sensors using 3D printing and replica moulding.

Flow sensors found in animals often feature soft and slender structures (e.g. fish neuromasts, insect hairs, mammalian stereociliary bundles, etc) that bend in response to the slightest flow disturbances in their surroundings and heighten the animal's vigilance with respect to prey and/or predators.

A model reduction method for biochemical reaction networks.

Background: In this paper we propose a model reduction method for biochemical reaction networks governed by a variety of reversible and irreversible enzyme kinetic rate laws, including reversible Michaelis-Menten and Hill kinetics. The method proceeds by a stepwise reduction in the number of complexes, defined as the left and right-hand sides of the reactions in the network. It is based on the Kron reduction of the weighted Laplacian matrix, which describes the graph structure of the complexes and reactions in the network.

Bayesian inference of the sites of perturbations in metabolic pathways via Markov chain Monte Carlo.

Motivation: Genetic modifications or pharmaceutical interventions can influence multiple sites in metabolic pathways, and often these are 'distant' from the primary effect. In this regard, the ability to identify target and off-target effects of a specific compound or gene therapy is both a major challenge and critical in drug discovery.

Results: We applied Markov Chain Monte Carlo (MCMC) for parameter estimation and perturbation identification in the kinetic modeling of metabolic pathways.