Using machine learning based on a random forest (RF) regression algorithm, we attempted to predict the amount of adsorbed serum protein on polymer brush films from the films’ physicochemical information and the monomers’ chemical structures constituting the films using a RF model. After the training of the RF model using the data of polymer brush films synthesized from five different types of monomers, the model became capable of predicting the amount of adsorbed protein from the chemical structure, physicochemical properties of monomer molecules, and structural parameters (density and thickness of the films). The analysis of the trained RF quantitatively provided the importance of each structural parameter and physicochemical properties of monomers toward serum protein adsorption (SPA). The ranking for the significance of the parameters agrees with our general understanding and perception. Based on the results, we discuss the correlation between brush film’s physical properties (such as thickness and density) and SPA and attempt to provide a guideline for the design of antibiofouling polymer brush films.

In this paper, we propose a new spectroscopic method to explore the behavior of molecules near polymeric molecular networks of water-containing soft materials such as hydrogels. We demonstrate the analysis of hydrogen bonding states of water in the vicinity of hydrogels (soft contact lenses). In this method, we apply force to hydrated contact lenses to deform them and to modulate the ratio between the signals from bulk and vicinal regions. We then collect spectra at different forces. Finally, we extracted the spectra of the vicinal region using the multivariate curve resolution-alternating least square (MCR-ALS) method. We report the hydration states depending on the chemical structures of hydrogels constituting the contact lenses.

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In this review, we summarize the current situation of materials informatics in the field of biomaterials. Different from other fields working on solid state materials, the functions of biomaterials are difficult to predict by using theoretical or computational approaches. Therefore, we need to collect data experimentally for the construction of dataset. We introduce our recent achievements on the prediction of protein adsorption onto self-assembled monolayers (SAMs) and polymer blush films. In addition, we describe the procedure of the data acquisition, selection of descriptors and algorithms.

We examined the surface structure, binding conditions, electrochemical behavior, and thermal stability of self-assembled monolayers (SAMs) on Au(111) formed by N-(2-mercaptoethyl)heptanamide (MEHA) containing an amide group in an inner alkyl chain using scanning tunneling microscopy (STM), X-ray photoelectron spectroscopy (XPS), and cyclic voltammetry (CV) to understand the effects of an internal amide group as a function of deposition time. The STM study clearly showed that the structural transitions of MEHA SAMs on Au(111) occurred from the liquid phase to the formation of a closely packed and well-ordered β-phase via a loosely packed α-phase as an intermediate phase, depending on the deposition time. XPS measurements showed that the relative peak intensities of chemisorbed sulfur against Au 4f for MEHA SAMs formed after deposition for 1 min, 10 min, and 1 h were calculated to be 0.0022, 0.0068, and 0.0070, respectively. Based on the STM and XPS results, it is expected that the formation of a well-ordered β-phase is due to an increased adsorption of chemisorbed sulfur and the structural rearrangement of molecular backbones to maximize lateral interactions resulting from a longer deposition period of 1 h. CV measurements showed a significant difference in the electrochemical behavior of MEHA and decanethiol (DT) SAMs as a result of the presence of an internal amide group in the MEHA SAMs. Herein, we report the first high-resolution STM image of well-ordered MEHA SAMs on Au(111) with a (3 × 2√3) superlattice (β-phase). We also found that amide-containing MEHA SAMs were thermally much more stable than DT SAMs due to the formation of internal hydrogen networks in MEHA SAMs. Our molecular-scale STM results provide new insight into the growth process, surface structure, and thermal stability of amide-containing alkanethiols on Au(111).