Machine-Learning-Aided Understanding of Protein Adsorption on Zwitterionic Polymer Brushes.

Constructing antifouling surfaces is a crucial technique for optimizing the performance of devices such as water treatment membranes and medical devices in practical environments. These surfaces are achieved by modification with hydrophilic polymers. Notably, zwitterionic (ZI) polymers have attracted considerable interest because of their ability to form a robust hydration layer and inhibit the adsorption of foulants. However, the importance of the molecular weight and density of the ZI polymer on the antifouling property is partially understood, and the surface design still retains an empirical flavor. Herein, we individually assessed the influence of the molecular weight and density of the ZI polymer on protein adsorption through machine learning. The results corroborated that protein adsorption is more strongly influenced by density than by molecular weight. Furthermore, the distribution of predicted protein adsorption against molecular weight and polymer density enabled us to determine conditions that enhanced (or weaken) antifouling. The relevance of this prediction method was also demonstrated by estimating the protein adsorption over a wide range of ionic strengths. Overall, this machine-learning-based approach is expected to contribute as a tool for the optimized functionalization of materials, extending beyond the applications of ZI polymer brushes.

Design of a highly sensitive and versatile membrane-based immunosensor using a Cu-free click reaction.

A highly sensitive immunosensor is developed using membrane pores as the recognition interface. In this sensor, a Cu-free click reaction is used to efficiently immobilize antibodies, and the sensor inhibits the adsorption of nonspecific proteins that degrade sensitivity. Furthermore, the sensor demonstrates rapid interleukin-6 detection in the picogram per milliliter range.

Numerical Modeling for Sensitive and Rapid Molecular Detection by Membrane-Based Immunosensors.

Rapid, simple, and sensitive point-of-care testing (POCT) has attracted attention in recent years due to its excellent potential for early disease diagnosis and health monitoring. The flow-through biosensor design is a candidate for POCT that utilizes the small-sized pores of a porous membrane as a recognition space where it emits a signal comparable to that of a conventional enzyme-linked immunosorbent assay within 35 min of detection time. In this paper, we present a numerical model for this immunosensing technology to systematically design an improved recognition system. The model considers mass transfer into the pore (convection and diffusion), the kinetics between the immobilized receptor and the target molecule, and the flow conditions, successfully leading to a bottleneck step (capture of secondary antibody) in sandwich-type detection. Our simulation results also show that this problem can be solved by adopting both appropriate receptors and analytical conditions. Eventually, the requirements to achieve the sensitivity required for POCT were fulfilled, which will allow for further development of immunosensing devices for disease detection.

Flow-Based Immunosensing Using the Pore Channel of a Porous Membrane As a Reaction Space.

This Letter describes a new rapid and sensitive immunosensing device using the pore space of a porous membrane as the reaction space. A track-etched membrane with uniform cylindrical pores is used as the base substrate of this device. The capture antibodies are covalently and densely immobilized inside the membrane pores by the uniform introduction of poly(acrylic acid) (PAAc) via the plasma graft polymerization technique, followed by the active ester method. This membrane shows excellent antibody retention by covalent binding. The detection test was carried out via a sandwich-type assay, and all reaction steps from the antigen-antibody reaction to the enzyme reaction were conducted by permeating each solution into the pores. The detection test showed a signal comparable to that of the conventional enzyme-linked immunosorbent assay, although the detection time required in the test was shortened to 35 min. The reason for achieving both high sensitivity and short detection time is that the antibody accumulated pore space with high selectivity and promoted contact between the reactants by solution permeation. This report is expected to aid the design of systems for membrane-based devices, which currently have problems associated with sensitivity, rapidity, selectivity, or amount of sample. We further expect that this system could be applied to various diagnostic areas, including point-of-care testing.

Control of Target Molecular Recognition in a Small Pore Space with Biomolecule-Recognition Gating Membrane.

A biomolecule-recognition gating membrane, which introduces thermosensitive graft polymer including molecular recognition receptor into porous membrane substrate, can close its pores by recognizing target biomolecule. The present study reports strategies for improving both versatility and sensitivity of the gating membrane. First, the membrane is fabricated by introducing the receptor via a selectively reactive click reaction improving the versatility. Second, the sensitivity of the membrane is enhanced via an active delivering method of the target molecules into the pores. In the method, the tiny signal of the target biomolecule is amplified as obvious pressure change. Furthermore, this offers 15 times higher sensitivity compared to the previously reported passive delivering method (membrane immersion to sample solution) with significantly shorter recognition time. The improvement will aid in applying the gating membrane to membrane sensors in medical fields.