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This work reports a new direction of natural lignin valorization, which utilizes lignin to produce crosslinked polycaprolactone (PCL) via a straightforward synthesis. Lignin's hydroxyl groups of its multibranched phenolic structure allow lignin to serve as crosslinkers, whereas the aromatic groups serve as hard segments. The modified natural lignin containing alkene terminals is crosslinked with a thiol-terminal PCL via Ru-catalyzed photoredox thiol-ene reaction. The high rate of gel contents measured for all crosslinked polymers, with the least being 84% of gel content, indicates efficient crosslinking. The prepared flat rectangular shape lignin-crosslinked PCL sample demonstrates rapid thermal responsive shape memory behavior at 10 C and 80 C showing interconversion between a permanent and temporary shape. The melting temperature of the lignin-crosslinked PCL is tunable by varying the percent weight of lignin. The 11, 21, and 30 wt % lignin demonstrated T m of 42 C, 35 C, and 26 C, respectively. The role of lignin as a crosslinker presented in this work suggests that lignin can serve as an efficient biomass-based functional additive to polymers.Additional supporting information may be found in the online version of this article.

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From the beginning of the usage of radiotherapy (RT) for cancer treatment, mathematical modeling has been integral to understanding radiobiology and for designing treatment approaches and schedules. There has been extensive modeling of response to RT with the inclusion of various degrees of biological complexity. Here we focus on models of tumor volume dynamics. There has been much discussion on the implications of different models of tumor growth, and it is just important to consider the implications of selecting different models for response to RT. In this study, we compare three models of tumor volume dynamics: (1) exponential growth with RT directly reducing tumor volume, (2) logistic growth with direct tumor volume reduction, and (3) logistic growth with RT reducing the tumor carrying capacity. For all three models, we: performed parameter sensitivity and identifiability analyses; investigated the impact of the parameter sensitivity on the tumor volume trajectories; and examined the rates of change in tumor volume (ΔV/Δt) during and RT treatment course. The parameter identifiability and sensitivity analyses revealed the interdependence of the different model parameters and may inform parameter calibration in any further usage of these models. In examining the ΔV/Δt trends, we coined a new metric – the point of maximum reduction of tumor volume (MRV) – to quantify the magnitude and timing of the expected largest impact of RT during a treatment course. Ultimately, the results of these analyses help us to better understand the implications of model selection while simultaneously generating many hypotheses about the underlying radiobiology that need to be tested on time-resolved measurements of tumor volume from appropriate pre-clinical or clinical data. The answers to these questions and more detailed study of these and similar models of tumor volume dynamics may enable more appropriate model selection on a disease-site or patient-by-patient basis.