This paper investigates with a statistical analysis the issue of performance reproducibility and optimization in fully inkjet-printed organic photodetectors on flexible substrates. The most crucial process step to obtain reproducible, well performing devices with a high process yield turns out to be the printing of the thin polyethylenimine interlayer used as a surface modifier for the bottom electrode. Controlling solution composition and deposition parameters for this layer, a 57 nA cm mean reverse dark current was achieved, with an outstanding standard deviation as low as 15 nA cm, with dramatic improvements in process yield (from less than 20% to over 90%). Device performance in terms of dark currents, EQE (from 50% up to 90% at 525 nm, depending on process), and rectification (ratio between forward current and reverse current over 10 and reaching 10 in the best cases) is among the best for fully printed detectors. Furthermore, the importance of relative humidity control in the deposition environment during the interlayer printing on device characteristics is reported, indicating the processing conditions optimal for scaling to mass manufacturing. The overall interlayer optimization approach was applied to a process using widely adopted materials in the organic optoelectronics field, and thus retains relevance on a broad range.

In recent years e-learning systems have promised to change the way people learn. However open issues still remain, in particular actual e-learning environments do not consider learning activities as part of the process of learning. Thus, it is not possible to define structured courses and specify precise learning paths apt to guide learners through learning materials. In our approach, we define courses as workflows. By so doing we can exploit powerful procedural rules in order to define precise while flexible learning paths. In this paper we present Virtual Campus, a research project sponsored by Microsoft Research (UK) and developed at Politecnico di Milano, that exploits a workflow engine to enacts the fruition of structured courses. Our platform provides both an authoring and a fruition environment. The former allows teachers to define and customize learning paths, publishing them as workflows. The fruition environment enacts the workflows and guides learners through the related learning paths. We also describe an experience in using the platform during a Software Engineering course composed by heterogeneous activities (lectures, studying activities, cooperative sessions of work, and exams).

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