Previous literature has consistently discussed reoccurring issues with conducting research in the gay and lesbian community and, for the purposes of this article, particularly the older lesbian community. Issues with sampling, including gaining access, ethical considerations, and conceptual definitions are ongoing struggles repeated within the literature. This article provides the experience of a research team in conducting such research and presents the viable solutions and ongoing barriers, as well as newer considerations that future research must take into account. In addition, this article provides the viewpoint of 189 older lesbians on the future research needs within their community.

There remains continued interest in improving the advanced water oxidation process [e.g., ultraviolet (UV)/hydrogen peroxide (H2O2)] for more efficient and environmentally friendly wastewater treatment. Here, we report the design, fabrication, and performance of graphene oxide (GO, on top)/nickel-doped iron oxyhydroxide (Ni:FeOOH, shell)/silicon nanowires (SiNWs, core) as a new multifunctional photocatalyst for the degradation of common pollutants like polystyrene and methylene blue through enhancing the hydroxyl radical (•OH) production rate of the UV/H2O2 system. The photocatalyst combines the advantages of a large surface area and light absorption characteristics of SiNWs with heterogeneous photo-Fenton active Ni:FeOOH and photocatalytically active/charge separator GO. In addition, the built-in electric field of GO/Ni:FeOOH/SiNWs facilitates the charge separation of electrons to GO and holes to Ni:FeOOH, thus boosting the photocatalytic performance. Our photocatalyst increases the •OH yield by 5.7 times compared with that of a blank H2O2 solution sample and also extends the light absorption spectrum to include visible light irradiation.

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Data‐driven, machine learning (ML)‐assisted approaches have been used to study structure‐property relationships at the atomic scale, which have greatly accelerated the screening process and new material discovery. However, such approaches are not easily applicable to modulating material properties of a soft material in a laboratory with specific ingredients. Moreover, it is desirable to relate material properties directly to the experimental recipes. Herein, a data‐driven approach to tailoring mechanical properties of a soft material is demonstrated using ML‐assisted predictions of mechanical properties based on experimental synthetic recipes. Polyurethane (PU) elastomer is used as a model soft material to demonstrate the approach and experimentally varied mechanical properties of the PU elastomer by modulating the mixing ratio between components of the elastomer. Twenty‐five experimental conditions are selected based on the design of experiment and use those data points to train a linear regression model. The resulting model takes desired mechanical properties as input and returns synthetic recipes of a soft material, which is subsequently validated by experiments. Lastly, the prediction accuracies of different machine learning algorithms is compared. It is believed that the approach is widely applicable to other material systems to establish experimental conditions and material property relationships for soft materials.