Research on polypeptide multilayer films, coatings, and microcapsules is located at the intersection of several disciplines: synthetic polymer chemistry and physics, biomaterials science, and nanoscale engineering. The past few years have witnessed considerable growth in each of these areas. Unexplored territory has been found at the borders, and new possibilities for technology development are taking form from technological advances in polypeptide production, sequencing of the human genome, and the nature of peptides themselves. Most envisioned applications of polypeptide multilayers have a biomedical bent. Prospects seem no less positive, however, in fields ranging from food technology to environmental science. This review of the present state of polypeptide multilayer film research covers key points of polypeptides as materials, means of polymer production and film preparation, film characterization methods, focal points of current research in basic science, and the outlook for a few specific applications. In addition, it discusses how the study of polypeptide multilayer films could help to clarify the physical basis of assembly and stability of polyelectrolyte multilayers, and mention is made of similarities to protein folding studies.

Estimating ecosystem evapotranspiration (ET) is important to understanding the global water cycle and to study land-atmosphere interactions. We developed a physics constrained machine learning (ML) model (hybrid model) to estimate latent heat flux (LE), which conserves the surface energy budget. By comparing model predictions with observations at 82 eddy covariance tower sites, our hybrid model shows similar performance to the pure ML model in terms of mean metrics (e.g., mean absolute percent errors) but, importantly, the hybrid model conserves the surface energy balance, while the pure ML model does not. A second key result is that the hybrid model extrapolates much better than the pure ML model, emphasizing the benefits of combining physics with ML for increased generalizations. The hybrid model allows inferring the structural dependence of ET and surface resistance (r s ), and we find that vegetation height and soil moisture are the main regulators of ET and r s . Plain Language SummaryA physics constrained machine learning model is developed using the FLUXNET2015 Tier 1 data set. This new approach is able to effectively retrieve latent heat flux while constraining energy conservation in the surface energy budget. This hybrid model has better performance in extrapolation than a pure machine learning model. Key Points: • A physics-constrained machine learning model of evapotranspiration (hybrid model) is developed and trained using the FLUXNET 2015 data set • The evapotranspiration retrieved by the hybrid model is as accurate as pure machine learning model and also conserves surface energy balance • The hybrid model better reproduces extremes and thus better extrapolates compared to the pure machine learning approach Supporting Information:• Supporting Information S1• Figure S1 • Table S1

Coenzyme Q(10) (CoQ(10) ) is a well-known antioxidant and has been used in many skincare products for anti-ageing purpose. However, the molecular mechanisms of CoQ(10) function in skin cells are not fully understood. In this paper, we compared the effects of CoQ(10) on primary human dermal fibroblasts from three individuals, including adult. We demonstrated that CoQ(10) treatment promoted proliferation of fibroblasts, increased type IV collagen expression and reduced UVR-induced matrix metalloproteinases-1 (MMP-1) level in embryonic and adult cells. In addition, CoQ(10) treatment increased elastin gene expression in cultured fibroblasts and significantly decreased UVR-induced IL-1α production in HaCat cells. Taken together, CoQ(10) presented anti-ageing benefits against intrinsic ageing as well as photo damage. Interestingly, CoQ(10) was able to inhibit tyrosinase activity, resulting in reduced melanin content in B16 cells. Thus, CoQ(10) may have potential depigmentation effects for skincare.

Simple molecular models predict key aspects of the "microscopic" assembly behavior of various peptide systems in the fabrication of multilayer films. Such films show substantial differences in density for different peptide systems. The data suggest that exponential film growth is possible in the absence of polymer diffusion and that "macroscopic" assembly behavior is more a function of peptide-peptide interactions than peptide sequence alone.