Nanoimprint Mold Consisting of an Adhesive Lap Joint between a Nanopatterned Metal Sleeve and a Carbon Composite Roll.

Roll-to-roll nanoimprinting is an emerging technology that has revolutionized the sectors of flexible electronics, thin films, and solar cells with its high throughput. However, there is still room for improvement. In this study, a FEM analysis (in ANSYS) was carried out on a large-area roll-to-roll nanoimprint system in which the master roller consists of a large nanopatterned nickel mold joined to a carbon fiber reinforced polymer (CFRP) base roller using epoxy adhesive. Deflections and pressure uniformity of the nano-mold assembly were analyzed under loadings of different magnitudes in a roll-to-roll nanoimprinting setup. Optimization of deflections was performed with applied loadings, and the lowest deflection value was 97.69 nm. The adhesive bond viability was assessed under a range of applied forces. Finally, potential strategies to reduce deflections were also discussed, which can be helpful in increasing pressure uniformity.

Utilizing EEG and fNIRS for the detection of sleep-deprivation-induced fatigue and its inhibition using colored light stimulation.

Drowsy driving is a common, but underestimated phenomenon in terms of associated risks as it often results in crashes causing fatalities and serious injuries. It is a challenging task to alert or reduce the driver's drowsy state using non-invasive techniques. In this study, a drowsiness reduction strategy has been developed and analyzed using exposure to different light colors and recording the corresponding electrical and biological brain activities. 31 subjects were examined by dividing them into 2 classes, a control group, and a healthy group. Fourteen EEG and 42 fNIRS channels were used to gather neurological data from two brain regions (prefrontal and visual cortices). Experiments shining 3 different colored lights have been carried out on them at certain times when there is a high probability to get drowsy. The results of this study show that there is a significant increase in HbO of a sleep-deprived participant when he is exposed to blue light. Similarly, the beta band of EEG also showed an increased response. However, the study found that there is no considerable increase in HbO and beta band power in the case of red and green light exposures. In addition to that, values of other physiological signals acquired such as heart rate, eye blinking, and self-reported Karolinska Sleepiness Scale scores validated the findings predicted by the electrical and biological signals. The statistical significance of the signals achieved has been tested using repeated measures ANOVA and t-tests. Correlation scores were also calculated to find the association between the changes in the data signals with the corresponding changes in the alertness level.

Process Optimization for Manufacturing Functional Nanosurfaces by Roll-to-Roll Nanoimprint Lithography.

Roll-to-roll nanoimprint lithography (RTR-NIL) is a low-cost and continuous fabrication process for large-area functional films. However, the partial ultraviolet (UV) resin filling obstructs the ongoing production process. This study incorporates UV resin filling process into the nanopillars and nanopores by using RTR-NIL. A multiphase numerical model with a sliding mesh method is proposed in this study to show the actual phenomena of imprint mold rotation and feeding of UV resin on the polyethylene terephthalate (PET) substrate. The implementation of UV resin filling under environmental conditions was performed by utilizing the open-channel (OC) boundary conditions. The numerical model was solved by using the explicit volume of fluid (VOF) scheme to compute the filling on each node of the computational domain. The effects of different processing parameters were investigated through the proposed numerical model such as imprinting speed (IS), contact angles (CAs), viscosity, initial thickness of the PET, and supporting roll diameter. A good agreement was found between numerical simulations and experimental results. The proposed numerical model gives better insights of the filling process for the mass production of functional surfaces with nanopillars and nanopores patterns for different applications on an industrial scale.

Nanofabrication Techniques: Challenges and Future Prospects.

Nanofabrication of functional micro/nano-features is becoming increasingly relevant in various electronic, photonic, energy, and biological devices globally. The development of these devices with special characteristics originates from the integration of low-cost and high-quality micro/nano-features into 3D-designs. Great progress has been achieved in recent years for the fabrication of micro/nanostructured based devices by using different imprinting techniques. The key problems are designing techniques/approaches with adequate resolution and consistency with specific materials. By considering optical device fabrication on the large-scale as a context, we discussed the considerations involved in product fabrication processes compatibility, the feature's functionality, and capability of bottom-up and top-down processes. This review summarizes the recent developments in these areas with an emphasis on established techniques for the micro/nano-fabrication of 3-dimensional structured devices on large-scale. Moreover, numerous potential applications and innovative products based on the large-scale are also demonstrated. Finally, prospects, challenges, and future directions for device fabrication are addressed precisely.

A Hybrid Speller Design Using Eye Tracking and SSVEP Brain-Computer Interface.

Steady-state visual evoked potentials (SSVEPs) have been extensively utilized to develop brain-computer interfaces (BCIs) due to the advantages of robustness, large number of commands, high classification accuracies, and information transfer rates (ITRs). However, the use of several simultaneous flickering stimuli often causes high levels of user discomfort, tiredness, annoyingness, and fatigue. Here we propose to design a stimuli-responsive hybrid speller by using electroencephalography (EEG) and video-based eye-tracking to increase user comfortability levels when presented with large numbers of simultaneously flickering stimuli. Interestingly, a canonical correlation analysis (CCA)-based framework was useful to identify target frequency with a 1 s duration of flickering signal. Our proposed BCI-speller uses only six frequencies to classify forty-eight targets, thus achieve greatly increased ITR, whereas basic SSVEP BCI-spellers use an equal number of frequencies to the number of targets. Using this speller, we obtained an average classification accuracy of 90.35 ± 3.597% with an average ITR of 184.06 ± 12.761 bits per minute in a cued-spelling task and an ITR of 190.73 ± 17.849 bits per minute in a free-spelling task. Consequently, our proposed speller is superior to the other spellers in terms of targets classified, classification accuracy, and ITR, while producing less fatigue, annoyingness, tiredness and discomfort. Together, our proposed hybrid eye tracking and SSVEP BCI-based system will ultimately enable a truly high-speed communication channel.

Approach to optimize 3-dimensional brain functional activation image with high resolution: a study on functional near-infrared spectroscopy.

In this study, 3-dimensional (3-D) enhanced brain-function-map generation and estimation methodology is presented. Optical signals were modelled in the form of numerical optimization problem to infer the best existing waveform of canonical hemodynamic response function. Inter-channel activity patterns were also estimated. The estimation of activation of inter-channel gap depends on the minimization of generalized cross-validation. 3-D brain activation maps were produced through inverse discrete cosine transform. The proposed algorithm acquired significant results for 3-D functional maps with high resolution, in comparison with that of 2-D functional t-maps. A comprehensive analysis by exhibiting images corresponding to several layers has also been appended.

Differential Path-Length Factor's Effect on the Characterization of Brain's Hemodynamic Response Function: A Functional Near-Infrared Study.

Functional near-infrared spectroscopy (fNIRS) has evolved as a neuro-imaging modality over the course of the past two decades. The removal of superfluous information accompanying the optical signal, however, remains a challenge. A comprehensive analysis of each step is necessary to ensure the extraction of actual information from measured fNIRS waveforms. A slight change in shape could alter the features required for fNIRS-BCI applications. In the present study, the effect of the differential path-length factor (DPF) values on the characteristics of the hemodynamic response function (HRF) was investigated. Results were compiled for both simulated data sets and healthy human subjects over a range of DPF values from three to eight. Different sets of activation durations and stimuli were used to generate the simulated signals for further analysis. These signals were split into optical densities under a constrained environment utilizing known values of DPF. Later, different values of DPF were used to analyze the variations of actual HRF. The results, as summarized into four categories, suggest that the DPF can change the main and post-stimuli responses in addition to other interferences. Six healthy subjects participated in this study. Their observed optical brain time-series were fed into an iterative optimization problem in order to estimate the best possible fit of HRF and physiological noises present in the measured signals with free parameters. A series of solutions was derived for different values of DPF in order to analyze the variations of HRF. It was observed that DPF change is responsible for HRF creep from actual values as well as changes in HRF characteristics.

Critical bounds on noise and SNR for robust estimation of real-time brain activity from functional near infra-red spectroscopy.

The robust characterization of real-time brain activity carries potential for many applications. However, the contamination of measured signals by various instrumental, environmental, and physiological sources of noise introduces a substantial amount of signal variance and, consequently, challenges real-time estimation of contributions from underlying neuronal sources. Functional near infra-red spectroscopy (fNIRS) is an emerging imaging modality whose real-time potential is yet to be fully explored. The objectives of the current study are to (i) validate a time-dependent linear model of hemodynamic responses in fNIRS, and (ii) test the robustness of this approach against measurement noise (instrumental and physiological) and mis-specification of the hemodynamic response basis functions (amplitude, latency, and duration). We propose a linear hemodynamic model with time-varying parameters, which are estimated (adapted and tracked) using a dynamic recursive least square algorithm. Owing to the linear nature of the activation model, the problem of achieving robust convergence to an accurate estimation of the model parameters is recast as a problem of parameter error stability around the origin. We show that robust convergence of the proposed method is guaranteed in the presence of an acceptable degree of model misspecification and we derive an upper bound on noise under which reliable parameters can still be inferred. We also derived a lower bound on signal-to-noise-ratio over which the reliable parameters can still be inferred from a channel/voxel. Whilst here applied to fNIRS, the proposed methodology is applicable to other hemodynamic-based imaging technologies such as functional magnetic resonance imaging.

Initial-Dip Existence and Estimation in Relation to DPF and Data Drift.

Early de-oxygenation (initial dip) is an indicator of the primal cortical activity source in functional neuro-imaging. In this study, initial dip's existence and its estimation in relation to the differential pathlength factor (DPF) and data drift were investigated in detail. An efficient algorithm for estimation of drift in fNIRS data is proposed. The results favor the shifting of the fNIRS signal to a transformed coordinate system to infer correct information. Additionally, in this study, the effect of the DPF on initial dip was comprehensively analyzed. Four different cases of initial dip existence were treated, and the resultant characteristics of the hemodynamic response function (HRF) for DPF variation corresponding to particular near-infrared (NIR) wavelengths were summarized. A unique neuro-activation model and its iterative optimization solution that can estimate drift in fNIRS data and determine the best possible fit of HRF with free parameters were developed and herein proposed. The results were verified on simulated data sets. The algorithm is applied to free available datasets in addition to six healthy subjects those were experimented using fNIRS and observations and analysis regarding shape of HRF were summarized as well. A comparison with standard GLM is also discussed and effects of activity strength parameters have also been analyzed.

Enhancement of Peripheral Nerve Regrowth by the Purine Nucleoside Analog and Cell Cycle Inhibitor, Roscovitine.

Peripheral nerve regeneration is a slow process that can be associated with limited outcomes and thus a search for novel and effective therapy for peripheral nerve injury and disease is crucial. Here, we found that roscovitine, a synthetic purine nucleoside analog, enhances neurite outgrowth in neuronal-like PC12 cells. Furthermore, ex vivo analysis of pre-injured adult rat dorsal root ganglion (DRG) neurons showed that roscovitine enhances neurite regrowth in these cells. Likewise, in vivo transected sciatic nerves in rats locally perfused with roscovitine had augmented repopulation of new myelinated axons beyond the transection zone. By mass spectrometry, we found that roscovitine interacts with tubulin and actin. It interacts directly with tubulin and causes a dose-dependent induction of tubulin polymerization as well as enhances Guanosine-5'-triphosphate (GTP)-dependent tubulin polymerization. Conversely, roscovitine interacts indirectly with actin and counteracts the inhibitory effect of cyclin-dependent kinases 5 (Cdk5) on Actin-Related Proteins 2/3 (Arp2/3)-dependent actin polymerization, and thus, causes actin polymerization. Moreover, in the presence of neurotrophic factors such as nerve growth factor (NGF), roscovitine-enhanced neurite outgrowth is mediated by increased activation of the extracellular signal-regulated kinases 1/2 (ERK1/2) and p38 mitogen-activated protein kinase (MAPK) pathways. Since microtubule and F-actin dynamics are critical for axonal regrowth, the ability of roscovitine to activate the ERK1/2 and p38 MAPK pathways and support polymerization of tubulin and actin indicate a major role for this purine nucleoside analog in the promotion of axonal regeneration. Together, our findings demonstrate a therapeutic potential for the purine nucleoside analog, roscovitine, in peripheral nerve injury.

Diverging Effects of Topographical Continuity on the Wettability of a Rough Surface.

The effects of the continuity of the surface pattern on wetting enhancement was investigated using micropillar and microhole arrays on hydrophilic and hydrophobic materials. Isolated micropillar arrays and continuous microhole arrays were prepared by a microscale imprinting technique using positive and negative Si molds fabricated by a conventional photolithography technique. The contact angles (CAs) and contact angle hysteresis (CAH) of the prepared surfaces were measured as a function of the surface parameter ξ, defined as the ratio of the top surface area of the microstructure to the surface area of the flat unit cell. It was found that the CAs of the micropillar array monotonically increased as the surface ratio decreased, regardless of the native wettability of the solid. However, an abnormal and consistent decrease of the CAs for the microhole array was observed when ξ < 0.5. To investigate the mechanism of this abnormality in wetting enhancement, the energy barriers for normal direction wetting, the so-called wetting transition from Cassi-Baxter (CB) wetting to Wenzel wetting, and lateral direction wetting, that is, spreading, were investigated with consideration of the trapped air in the microhole. The analysis unveiled that the hydrophobicity of the hydrophilic surfaces are attributable to the liquid-air interface pinning at the discontinuous edge of the pillar, which results in CB wetting. The abnormal decrease in the CAs of the microhole-patterned surfaces with ξ < 0.5 has been attributed to the relatively low energy barrier for spreading influenced by the continuity of the three-phase contact line. Additionally, trapped air in the microhole also plays a role in the spreading of water droplets by hindering the wetting transition from CB wetting to Wenzel wetting.

Cortical Signal Analysis and Advances in Functional Near-Infrared Spectroscopy Signal: A Review.

Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging modality that measures the concentration changes of oxy-hemoglobin (HbO) and de-oxy hemoglobin (HbR) at the same time. It is an emerging cortical imaging modality with a good temporal resolution that is acceptable for brain-computer interface applications. Researchers have developed several methods in last two decades to extract the neuronal activation related waveform from the observed fNIRS time series. But still there is no standard method for analysis of fNIRS data. This article presents a brief review of existing methodologies to model and analyze the activation signal. The purpose of this review article is to give a general overview of variety of existing methodologies to extract useful information from measured fNIRS data including pre-processing steps, effects of differential path length factor (DPF), variations and attributes of hemodynamic response function (HRF), extraction of evoked response, removal of physiological noises, instrumentation, and environmental noises and resting/activation state functional connectivity. Finally, the challenges in the analysis of fNIRS signal are summarized.

Hybrid EEG--Eye Tracker: Automatic Identification and Removal of Eye Movement and Blink Artifacts from Electroencephalographic Signal.

Contamination of eye movement and blink artifacts in Electroencephalogram (EEG) recording makes the analysis of EEG data more difficult and could result in mislead findings. Efficient removal of these artifacts from EEG data is an essential step in improving classification accuracy to develop the brain-computer interface (BCI). In this paper, we proposed an automatic framework based on independent component analysis (ICA) and system identification to identify and remove ocular artifacts from EEG data by using hybrid EEG and eye tracker system. The performance of the proposed algorithm is illustrated using experimental and standard EEG datasets. The proposed algorithm not only removes the ocular artifacts from artifactual zone but also preserves the neuronal activity related EEG signals in non-artifactual zone. The comparison with the two state-of-the-art techniques namely ADJUST based ICA and REGICA reveals the significant improved performance of the proposed algorithm for removing eye movement and blink artifacts from EEG data. Additionally, results demonstrate that the proposed algorithm can achieve lower relative error and higher mutual information values between corrected EEG and artifact-free EEG data.

Parallel Imaging of 3D Surface Profile with Space-Division Multiplexing.

We have developed a modified optical frequency domain imaging (OFDI) system that performs parallel imaging of three-dimensional (3D) surface profiles by using the space division multiplexing (SDM) method with dual-area swept sourced beams. We have also demonstrated that 3D surface information for two different areas could be well obtained in a same time with only one camera by our method. In this study, double field of views (FOVs) of 11.16 mm × 5.92 mm were achieved within 0.5 s. Height range for each FOV was 460 µm and axial and transverse resolutions were 3.6 and 5.52 µm, respectively.

Microwave Sintering of Silver Nanoink for Radio Frequency Applications.

Microwave sintering is a promising method for low-temperature processes, as it provides advantages such as uniform, fast, and volumetric heating. In this study, we investigated the electrical characteristics of inkjet-printed silver (Ag) circuits sintered by microwaves. The microstructural evolutions of inkjet-printed Ag circuits sintered at various temperatures for different durations were observed with a field emission scanning electron microscope. The electrical properties of the inkjet-printed Ag circuits were analysed by electrical resistivity measurements and radio frequency properties including scattering-parameters in the frequency range of 20 MHz to 20 GHz. The experimental results show that the signal losses of the Ag circuits sintered by microwave heating were lower than those sintered by conventional heating as microwave heating led to granular films which were nearly fully sintered without pores on the surfaces. When the inkjet-printed Ag circuits were sintered by microwaves at 300 °C for 4 min, their electrical resistivity was 5.1 µΩ cm, which is 3.2 times larger than that of bulk Ag. Furthermore, microwave sintering at 150 °C for 4 min achieved much lower signal losses (1.1 dB at 20 GHz) than conventional sintering under the same conditions.

Optimal hemodynamic response model for functional near-infrared spectroscopy.

Functional near-infrared spectroscopy (fNIRS) is an emerging non-invasive brain imaging technique and measures brain activities by means of near-infrared light of 650-950 nm wavelengths. The cortical hemodynamic response (HR) differs in attributes at different brain regions and on repetition of trials, even if the experimental paradigm is kept exactly the same. Therefore, an HR model that can estimate such variations in the response is the objective of this research. The canonical hemodynamic response function (cHRF) is modeled by two Gamma functions with six unknown parameters (four of them to model the shape and other two to scale and baseline respectively). The HRF model is supposed to be a linear combination of HRF, baseline, and physiological noises (amplitudes and frequencies of physiological noises are supposed to be unknown). An objective function is developed as a square of the residuals with constraints on 12 free parameters. The formulated problem is solved by using an iterative optimization algorithm to estimate the unknown parameters in the model. Inter-subject variations in HRF and physiological noises have been estimated for better cortical functional maps. The accuracy of the algorithm has been verified using 10 real and 15 simulated data sets. Ten healthy subjects participated in the experiment and their HRF for finger-tapping tasks have been estimated and analyzed. The statistical significance of the estimated activity strength parameters has been verified by employing statistical analysis (i.e., t-value > t critical and p-value < 0.05).

Upper bounds on noise and model misspecification for robust estimation of real-time brain activity from functional near infrared spectroscopy.

The robust characterization of real-time brain activity carries potential for many applications. However, the contamination of measured signals by various instrumental, environmental, and physiological sources of noise introduces a substantial amount of signal variance and, consequently, challenges real-time estimation of contributions from underlying neuronal sources. Functional near infrared spectroscopy (fNIRS) is an emerging imaging modality whose real-time potential is yet to be fully explored. The objectives of the current study are to (i) validate a time-dependent linear model of hemodynamic responses in fNIRS, and (ii) test the robustness of this approach against measurement noise (instrumental and physiological) and mis-specification of the hemodynamic response basis functions (amplitude, latency, and duration). We propose a linear hemodynamic model with time-varying parameters, which are estimated (adapted and tracked) using a dynamic recursive least square algorithm. Owing to the linear nature of the activation model, the problem of achieving robust convergence to an accurate estimation of the model parameters is recast as a problem of parameter error stability around the origin. We show that robust convergence of the proposed method is guaranteed in the presence of an acceptable degree of model misspecification and we derive an upper bound on noise under which reliable parameters can still be inferred. While here applied to fNIRS, the proposed methodology is applicable to other hemodynamic-based imaging technologies such as functional magnetic resonance imaging.

Nanostructured multifunctional surface with antireflective and antimicrobial characteristics.

Functional polymeric films with antireflective and hydrophobic properties have been widely used for electronic device displays. However, the design of such functional films with an antimicrobial characteristic has been a challenge. We designed a nanostructured surface using a rigorous coupled-wave analysis to obtain a period of 300 nm and an aspect ratio of 3.0 on a flat poly(methyl methacrylate) film. The fabricated nanostructure was hydrophobic and exhibited the desired optical characteristics with a reflectance of less than 0.5% over the visible wavelength range. Furthermore, the nanoimprinted polymer film exhibited antimicrobial characteristics and low adhesion when compared with the corresponding flat surface. The results suggest that the nanostructured surface designed in this study is multifunctional and should be suitable for the production of protective optical and hygienic polymer films for the displays of portable electronic devices.

Wavelength-spacing tuning of a dual-wavelength active-mode locking fiber laser.

We demonstrate a novel concept of wavelength-spacing tuning of a dual-wavelength active-mode locking fiber laser in the 1.3-µm wavelength region. A dual-cavity configuration is implemented using a chirped fiber Bragg grating pair for wavelength-spacing tuning. Both normal and anomalous dispersion cavities were schematically designed for wavelength-spacing tuning ranging from 3.52-33.54 nm. The side-mode suppression ratio was ∼30 dB for both wavelengths. Because of the filter-less tuning mechanism, a fast repletion rate of wavelength-spacing sweeping and switching was demonstrated for frequencies up to 50 kHz.

WITHDRAWN: Robustness bound on noises with mis-modeling errors for signified brain imaging in real-time.

This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy.