Elucidating metal oxide growth mechanisms is essential for precisely designing and fabricating nanostructured oxides with broad applications in energy and electronics. However, current epitaxial oxide growth methods are based on macroscopic empirical knowledge, lacking fundamental guidance at the nanoscale. Using correlated in situ environmental transmission electron microscopy, statistically-validated quantitative analysis, and density functional theory calculations, we show epitaxial Cu2O nano-island growth on Cu is layer-by-layer along Cu2O(110) planes, regardless of substrate orientation, contradicting classical models that predict multi-layer growth parallel to substrate surfaces. Growth kinetics show cubic relationships with time, indicating individual oxide monolayers follow Frank-van der Merwe growth whereas oxide islands follow Stranski-Krastanov growth. Cu sources for island growth transition from step edges to bulk substrates during oxidation, contrasting with classical corrosion theories which assume subsurface sources predominate. Our results resolve alternative epitaxial island growth mechanisms, improving the understanding of oxidation dynamics critical for advanced manufacturing at the nanoscale.
Increasingly, funding agencies are investing in integrated and transdisciplinary research to tackle “grand challenge” priority areas, critical for sustaining agriculture and protecting the environment. Coordinating multidisciplinary research teams capable of addressing these priority areas, however, presents its own unique set of challenges, ranging from bridging across multiple disciplinary perspectives to achieve common questions and methods to facilitating engagement in holistic and integrative thinking that promotes linkages from scholarship to societal needs. We propose that structural equation modeling (SEM) can provide a powerful framework for synergizing multidisciplinary research teams around grand challenge issues. Structural equation modeling can integrate both visual and statistical expression of complex hypotheses at all stages of the research process, from planning to analysis. Three elements of the SEM framework are particularly beneficial to multidisciplinary research teams; these include (i) a common graphical language that transcends disciplinary boundaries, (ii) iterative, critical evaluation of complex hypotheses involving manifest and latent variables and direct and indirect interactions, and (iii) enhanced opportunities to discover unanticipated interactions or causal pathways as empirical data are tested statistically against the model. Using our ongoing multidisciplinary, multisite field investigation of climate change adaptation and mitigation in annual row crop agroecosystems as a case study, we demonstrate the value of the SEM framework for project design, coordination, and implementation and provide recommendations for its broader application as a means to more effectively engage and address issues of critical societal concern.
Background Cancer treatments can cause a variety of symptoms that impair quality of life and functioning but are frequently missed by clinicians. Smartphone and wearable sensors may capture behavioral and physiological changes indicative of symptom burden, enabling passive and remote real-time monitoring of fluctuating symptoms Objective The aim of this study was to examine whether smartphone and Fitbit data could be used to estimate daily symptom burden before and after pancreatic surgery. Methods A total of 44 patients scheduled for pancreatic surgery participated in this prospective longitudinal study and provided sufficient sensor and self-reported symptom data for analyses. Participants collected smartphone sensor and Fitbit data and completed daily symptom ratings starting at least two weeks before surgery, throughout their inpatient recovery, and for up to 60 days after postoperative discharge. Day-level behavioral features reflecting mobility and activity patterns, sleep, screen time, heart rate, and communication were extracted from raw smartphone and Fitbit data and used to classify the next day as high or low symptom burden, adjusted for each individual’s typical level of reported symptoms. In addition to the overall symptom burden, we examined pain, fatigue, and diarrhea specifically. Results Models using light gradient boosting machine (LightGBM) were able to correctly predict whether the next day would be a high symptom day with 73.5% accuracy, surpassing baseline models. The most important sensor features for discriminating high symptom days were related to physical activity bouts, sleep, heart rate, and location. LightGBM models predicting next-day diarrhea (79.0% accuracy), fatigue (75.8% accuracy), and pain (79.6% accuracy) performed similarly. Conclusions Results suggest that digital biomarkers may be useful in predicting patient-reported symptom burden before and after cancer surgery. Although model performance in this small sample may not be adequate for clinical implementation, findings support the feasibility of collecting mobile sensor data from older patients who are acutely ill as well as the potential clinical value of mobile sensing for passive monitoring of patients with cancer and suggest that data from devices that many patients already own and use may be useful in detecting worsening perioperative symptoms and triggering just-in-time symptom management interventions.
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