A pitfall for classification

High-efficiency kesterite-based thin film solar cells typically feature Cu- poor, Zn-rich absorbers although secondary phases occur easily in non- stoichiometric Cu2ZnSnSe4. We therefore applied high-resolution X-ray fluorescence analysis using a synchrotron nanobeam to study the local composition of a CZTSe cross section lamella cut from a sample with an integral composition of Zn/Sn = 1.37 and Cu/(Zn+Sn) = 0.55. We find submicrometer-sized ZnSe-, SnSe/SnSe2-, and even CuSe/Cu2Se-like secondary phases, while the local compositions of the kesterite are highly Zn-rich yet barely Cu-poor with 1.5 ≤ Zn/Sn ≤ 2.2 and Cu/(Zn+Sn) ∼ 1.0. Consequently, great care must be taken when relating the integral composition to other material properties including the device performance

Mapping Drug Physico-Chemical Features to Pathway Activity Reveals Molecular Networks Linked to Toxicity Outcome

The identification of predictive biomarkers is at the core of modern toxicology. So far, a number of approaches have been proposed. These rely on statistical inference of toxicity response from either compound features (i.e., QSAR), in vitro cell based assays or molecular profiling of target tissues (i.e., expression profiling). Although these approaches have already shown the potential of predictive toxicology, we still do not have a systematic approach to model the interaction between chemical features, molecular networks and toxicity outcome. Here, we describe a computational strategy designed to address this important need. Its application to a model of renal tubular degeneration has revealed a link between physico-chemical features and signalling components controlling cell communication pathways, which in turn are differentially modulated in response to toxic chemicals. Overall, our findings are consistent with the existence of a general toxicity mechanism operating in synergy with more specific single-target based mode of actions (MOAs) and provide a general framework for the development of an integrative approach to predictive toxicology