Control over the out-of-plane molecular orientation of solution-processed organic semiconductors is a long-standing challenge in the organic electronics community. Here, a generalizable strategy using nanoconfinement to direct the nucleation of small-molecule organic semiconductors during solution-phase deposition is presented. Using a facile dip-coating process, triisopropylsilylethynylderivatized acene molecules were deposited onto nanoporous anodized aluminum oxide (AAO) scaffolds with average pore diameters ranging from 60 to 200 nm. Preferentially oriented nuclei were found to form within the cylindrical AAO nanopores such that the fast growth direction (i.e., the π-stack direction) aligned with the long axes of the pores. Crystal growth then propagated above the scaffold, resulting in the formation of vertical crystal arrays with the high surface energy π-planes exposed at the crystal tips. The diameters and heights of these crystals were tunable over ranges of 100−600 nm and 0.8−6.7 μm, respectively, by varying the dip-coating speed and scaffold pore diameters. Photoluminescence (PL) experiments further revealed an 8-fold enhancement of the PL signal from vertical crystal arrays compared to horizontal crystals deposited on flat SiO 2 substrates due to waveguiding along the crystal length. Critically, this strategy is compatible with continuous deposition techniques that will enable the high-throughput, large-area manufacturing of flexible and inexpensive optoelectronic devices.

Objectives Geriatric clinical care is a multidisciplinary assessment designed to evaluate older patients’ (age 65 years and above) functional ability, physical health, and cognitive well-being. The majority of these patients suffer from multiple chronic conditions and require special attention. Recently, hospitals utilize various artificial intelligence (AI) systems to improve care for elderly patients. The purpose of this systematic literature review is to understand the current use of AI systems, particularly machine learning (ML), in geriatric clinical care for chronic diseases. Materials and Methods We restricted our search to eight databases, namely PubMed, WorldCat, MEDLINE, ProQuest, ScienceDirect, SpringerLink, Wiley, and ERIC, to analyze research articles published in English between January 2010 and June 2019. We focused on studies that used ML algorithms in the care of geriatrics patients with chronic conditions. Results We identified 35 eligible studies and classified in three groups: psychological disorder (n = 22), eye diseases (n = 6), and others (n = 7). This review identified the lack of standardized ML evaluation metrics and the need for data governance specific to health care applications. Conclusion More studies and ML standardization tailored to health care applications are required to confirm whether ML could aid in improving geriatric clinical care.