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ABSTRACT The thermal transitions of organic semiconductors have important implications for morphological and thermomechanical properties of electronic devices. Here we show that in situ UV–vis spectroscopy coupled with automated data analysis is a facile tool to determine the numerous thermal transitions that can exist in organic semiconductor thin films. Thermal transitions are automatically identified through employing linear segment detection of the semilogarithmic normalized deviation metric (NDM) as well as the spectral changes using piecewise linear regression. Furthermore, by comparing the in situ UV–vis with other thermal measurement methods we observe the temperature dependent absorbance can be used to discern the type of thermal transitions including liquid crystal transitions, glass transitions, and melting temperatures. This approach is used to characterize five well‐known conjugated polymers and a small molecule semiconductor, each with distinct thermal phase behavior, where we show good agreement with other thermal characterization methods. Thus, the method we introduce, which we refer to asAutomatedThermal transition identification usingLinear regression analysis of theAbsorbanceSpectra (ATLAS), is shown to be an effective and simple screening tool to identify multiple thermal transitions in thin films. This is a high‐throughput thermal analysis approach that will assist in advancing structure‐morphology‐function relations in organic electronics. 

Greenhouses conserve land and water while increasing crop production, making them an attractive system for low environmental impact agriculture. Yet, to achieve this goal, there is a need to reduce their large energy demand. Employing semitransparent organic solar cells (OSCs) on greenhouse structures provide an opportunity to offset the greenhouse energy needs while maintaining the lighting needs of the plants. However, the design trade-off involved in optimizing solar power generation and crop productivity to maximize greenhouse economic value is yet to be studied in detail. Here, a functional plant growth model is integrated with a dynamic energy model that includes supplemental lighting to optimize the economics of growing lettuce and tomato. The greenhouse optimization considers 64 different OSC active layers with varying roof coverage for 25 distinct climates providing a global perspective. We find that crop yield is the primary economic driver, and that crop yield can be maintained in OSC-greenhouses across diverse climates. The crop productivity along with the energy produced by the OSCs results in improved net present value of the OSC-greenhouses relative to conventional systems in most climates for both lettuce and tomato. In addition, we find common solar cell active layers that maximize greenhouse economic value resulting in guidelines for scaling up OSC-greenhouse design. Through this model framework, we highlight the opportunity for OSCs in greenhouses, uncover designs and locations that provide the most value, and provide a basis for further development of OSC-greenhouses to achieve a sustainable means of food production.