Tip-enhanced photoluminescence nano-spectroscopy and nano-imaging

Photoluminescence (PL), a photo-excited spontaneous emission process, provides a wealth of optical and electronic properties of materials, which enable microscopic and spectroscopic imaging, biomedical sensing and diagnosis, and a range of photonic device applications. However, conventional far-field PL measurements have limitations in sensitivity and spatial resolution, especially to investigate single nano-materials or nano-scale dimension of them. In contrast, tip-enhanced photoluminescence (TEPL) nano-spectroscopy provides an extremely high sensitivity with <10 nm spatial resolution, which allows the desired nano-scale characterizations. With outstanding and unique optical properties, low-dimensional quantum materials have recently attracted much attention, and TEPL characterizations, i. e., probing and imaging, and even control at the nanoscale, have been extensively studied. In this review, we discuss the fundamental working mechanism of PL enhancement by plasmonic tip, and then highlight recent advances in TEPL studies for low-dimensional quantum materials. Finally, we discuss several remaining challenges of TEPL nano-spectroscopy and nano-imaging, such as implementation in non-ambient media and in situ environments, limitations in sample structure, and control of near-field polarization, with perspectives of the approach and its applications

Conformational heterogeneity of molecules physisorbed on a gold surface at room temperature

A quantitative single-molecule tip-enhanced Raman spectroscopy (TERS) study at room temperature remained a challenge due to the rapid structural dynamics of molecules exposed to air. Here, we demonstrate the hyperspectral TERS imaging of single or a few brilliant cresyl blue (BCB) molecules at room temperature, along with quantitative spectral analyses. Robust chemical imaging is enabled by the freeze-frame approach using a thin Al2O3 capping layer, which suppresses spectral diffusions and inhibits chemical reactions and contamination in air. For the molecules resolved spatially in the TERS image, a clear Raman peak variation up to 7.5 cm(-1) is observed, which cannot be found in molecular ensembles. From density functional theory-based quantitative analyses of the varied TERS peaks, we reveal the conformational heterogeneity at the single-molecule level. This work provides a facile way to investigate the single-molecule properties in interacting media, expanding the scope of single-molecule vibrational spectroscopy studies. Tip-enhanced vibrational spectroscopy at room temperature is complicated by molecular conformational dynamics, photobleaching, contaminations, and chemical reactions in air. This study demonstrates that a sub-nm protective layer of Al2O3 provides robust conditions for probing single-molecule conformations

A Pedagogical Framework for Mobile Learning: Categorizing Educational Applications of Mobile Technologies into Four Types

Instructional designers and educators recognize the potential of mobile technologies as a learning tool for students and have incorporated them into the distance learning environment. However, little research has been done to categorize the numerous examples of mobile learning in the context of distance education, and few instructional design guidelines based on a solid theoretical framework for mobile learning exist. In this paper I compare mobile learning (m-learning) with electronic learning (e-learning) and ubiquitous learning (u-learning) and describe the technological attributes and pedagogical affordances of mobile learning presented in previous studies. I modify transactional distance (TD) theory and adopt it as a relevant theoretical framework for mobile learning in distance education. Furthermore, I attempt to position previous studies into four types of mobile learning: 1) high transactional distance socialized m-learning, 2) high transactional distance individualized m-learning, 3) low transactional distance socialized m-learning, and 4) low transactional distance individualized m-learning. As a result, this paper can be used by instructional designers of open and distance learning to learn about the concepts of mobile learning and how mobile technologies can be incorporated into their teaching and learning more effectively

Development of the Learning Analytics Dashboard to Support Students' Learning Performance

The Learning Analytics Dashboard (LAD) is an application to show students' online behavior patterns in a virtual learning environment. This supporting tool works by tracking students_ log-files, mining massive amounts of data to find meaning, and visualizing the results so they can be comprehended at a glance. This paper reviews previously developed applications to analyze their features. Based on the implications from the review of previous studies as well as a preliminary investigation on the need for such tools, an early version of the LAD was designed and developed. Also, in order to improve the LAD, a usability test incorporating a stimulus recall interview was conducted with 38 college students in two blended learning classes. Evaluation of this tool was performed in an experimental research setting with a control group and additional surveys were conducted asking students’ about perceived usefulness, conformity, level of understanding of graphs, and their behavioral changes. The results indicated that this newly developed learning analytics tool did not significantly impact on their learning achievement. However, lessons learned from the usability and pilot tests support that visualized information impacts on students’ understanding level; and the overall satisfaction with dashboard plays as a covariant that impacts on both the degree of understanding and students' perceived change of behavior. Taking in the results of the tests and students' open-ended responses, a scaffolding strategy to help them understand the meaning of the information displayed was included in each sub section of the dashboard. Finally, this paper discusses future directions in regard to improving LAD so that it better supports students_ learning performance, which might be helpful for those who develop learning analytics applications for students

Real-Time Adaptive Management of Soil Salinity Using a Receding Horizon Control Algorithm: A Pilot-Scale Demonstration

This work demonstrates the application of real-time adaptive management principles to the problem of controlling the salinity levels in, and/or protecting groundwater quality beneath, soils undergoing irrigation with relatively saline water (e.g., reclaimed wastewater) under arid/semi- arid conditions. Here, optimal feedback-control scheme known as Receding Horizon Control (RHC) previously applied offline to control soil moisture levels during irrigation (Park et al., 2009) is applied inline during a pilot-scale field test aimed at balancing reclaimed water reuse and soil/groundwater quality in real-time. RHC is supported by sensor measurements, physically-based state prediction models, and optimization algorithms to drive field conditions to a desired environmental state. A simulation model including a one-dimensional (vertical) form of the Richards equation coupled to energy and solute transport equations is employed as a state estimator to provide predicted soil moisture, temperature, and salinity data. Vertical multi-sensor arrays installed in the soil provide initial conditions and continuous feedback to the control scheme. An optimization algorithm determines the optimal irrigation rate and frequency based on the imposed salinity constraints while forced by the requirement to maximize water reuse. The small-scale field test demonstrated that the RHC scheme was capable of maintaining specified salt levels at a prescribed soil depth autonomously. This finding suggests that, given an adequately structured and trained simulation model, sensor networks, prediction models, and optimization algorithms can be incorporated in the context of RHC to achieve water reuse and agricultural objectives while minimizing negative impacts on environmental quality autonomously

Automatic Feedback Control In Support of Sensor Networks to Monitor Nitrate in Palmdale and in Merced Backyard (CON 2)

In an effort to balance water reuse and protect groundwater quality when irrigating with reclaimed water, an adaptive management scheme with feedback control has been developed and is currently undergoing testing. Several multi-level sensing arrays (pylons) equipped with soil moisture, temperature, and nitrate sensors are installed in Palmdale and in Merced backyard. The pylon is coupled to a simulation and management algorithm to optimize irrigation scheduling. Specifically, a nonlinear programming-based control algorithm, referred to as Receding Horizon Feedback Control (RHFC), is proposed to maximize water reuse and maintain nitrate concentration in groundwater below the regulatory threshold. Each pylon supplies the irrigation scheduling algorithm with real-time field information about water infiltration and distribution, nitrate propagation, and heat transport (in support of evaporation estimates). A test bed for Palmdale deployment is set up in Merced backyard. An automatic flow controller system for sprinkling is equipped with data acquisition board to interface with sensors and management algorithm in MATLABTM. One-dimensional simulators are used to estimate key local soil hydraulic and transport parameters in near real time

Real-time Model Parameter Estimation for Analyzing Transport in Porous Media

This work describes the integration of data acquisition hardware and software for the purpose acquiring not only data, but real-time transport model parameter estimates in the context of subsurface flow and transport problems. Integrated data acquisitionparameter estimation systems can be used to reduce data storage requirements, trigger event recognition and/or more detailed sampling actions, and otherwise enhance remote monitoring capabilities. The contaminant transport problem is posed here as the analogous heat transfer problem in a three-dimensional, intermediate-scale physical aquifer model. A constant source of warm water is fed into a sandy aquifer undergoing steady, unidirectional flow. The spatial distribution of temperature in the medium is monitored over time using 17 thermocouples embedded in the medium. These sensors log temperatures via conventional analog-to-digital conversion hardware driven by commercially available data acquisition software (LabVIEW™). Parameter estimation routines programmed in MATLAB™-based M-files are embedded in the LabVIEW data acquisition routine and access parameter estimation libraries, such as the descent method employed here, via the Internet. The integrated data acquisition-parameter estimation system is demonstrated for the estimation of (1) the thermal dispersion coefficients (analogous to mass dispersion coefficients), given a known heat source, and (2) the location of a heat source, given known thermal dispersion coefficients. In both cases, the parameter estimation procedure is executed repeatedly as the data are acquired. For the case of source location, the effect of the number of sensors on the parameter estimation procedure is also demonstrated. Reasonable parameter estimates are provided rapidly during both the transient and steady state phases of the experiments, with accuracy increasing with time and with the number of observations employed

A Receding Horizon Control Algorithm for Adaptive Management of Soil Moisture and Chemical Levels during Irrigation

The capacity to adaptively manage irrigation and associated contaminant transport is desirable from the perspectives of water conservation, groundwater quality protection, and other concerns. This paper introduces the application of a feedback-control strategy known as Receding Horizon Control (RHC) to the problem of irrigation management. The RHC method incorporates sensor measurements, predictive models, and optimization algorithms to maintain soil moisture at certain levels or prevent contaminant propagation beyond desirable thresholds. Theoretical test cases are first presented to examine the RHC scheme performance for the control of soil moisture and nitrate levels in a soil irrigation problem. Then, soil moisture control is successfully demonstrated for a center-pivot system in Palmdale, CA where reclaimed water is used for agricultural irrigation. Real-time soil moisture, temperature, and meteorological data are streamed wirelessly to a field computer to enable autonomous execution of the RHC algorithm. The RHC scheme is demonstrated to be a viable strategy for achieving water reuse and agricultural objectives while minimizing negative impacts on environmental quality