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Abstract A method for in situ photografting during direct laser writing by two-photon polymerization is presented. The technique serves as a powerful approach to the formation of covalent bonds between 3D photoresist structures and thermoplastic surfaces. By leveraging the same laser for both pattern generation and localized surface reactions, crosslinking between the bulk photoresist and thermoplastic surface is achieved during polymerization. When applied to in-channel direct laser writing for microfluidic device fabrication, the process yields exceptionally strong adhesion and robust bond interfaces that can withstand pressure gradients as high as 7 MPa through proper channel design, photoinitiator selection, and processing conditions. 

It is common to quantify causal effects with mean values, which, however, may fail to capture significant distribution differences of the outcome under different treatments. We study the problem of estimating the density of the causal effect of a binary treatment on a continuous outcome given a binary instrumental variable in the presence of covariates. Specifically, we consider the local treatment effect, which measures the effect of treatment among those who comply with the assignment under the assumption of monotonicity (only the ones who were offered the treatment take it). We develop two families of methods for this task, kernel-smoothing and model-based approximations -- the former smoothes the density by convoluting with a smooth kernel function; the latter projects the density onto a finite-dimensional density class. For both approaches, we derive double/debiased machine learning (DML) based estimators. We study the asymptotic convergence rates of the estimators and show that they are robust to the biases in nuisance function estimation. We illustrate the proposed methods on synthetic data and a real dataset called 401(k). 

From a design-based research study with 31 families, we share the design conjectures that guided the first two iterations of research. The team developed a mobile augmented reality app focused on water-rock interactions to make earth sciences appealing to rural families. We iterated on one design element, the augmented reality visualizations, to understand how these AR elements influence families’ learning behavior in a children’s garden cave as well as their resulting geosciences knowledge. This analysis is an example of how design conjecture maps can be used to support research and development of mobile computer-supported collaborative learning opportunities for families in outdoor, informal learning settings.