3D Microfluidic Devices in a Single Piece of Paper for the Simultaneous Determination of Nitrite and Thiocyanate.

The concentrations of nitrite and thiocyanate in saliva can be used as the biomarkers of the progression of periodontitis disease and environmental tobacco smoke exposure, respectively. Therefore, it is particularly necessary to detect these two indicators in saliva. Herein, the three-dimensional single-layered paper-based microfluidic analytical devices (3D sl-µPADs) were, for the first time, fabricated by the spraying technique for the colorimetric detection of nitrite and thiocyanate at the same time. The conditions for 3D sl-µPADs fabrication were optimized in order to well control the penetration depth of the lacquer in a paper substrate. Then, the developed 3D sl-µPADs were utilized to simultaneously detect nitrite and thiocyanate and the limits of detection are 0.0096 and 0.074 mM, respectively. What is more, the µPADs exhibited good specificity, good repeatability, and acceptable recoveries in artificial saliva. Therefore, the developed 3D sl-µPADs show a great potential to determine nitrite and thiocyanate for the assessment of the human health.

High-power high-brightness 980 nm lasers with >50% wall-plug efficiency based on asymmetric super large optical cavity.

High-power high-brightness super large optical cavity laser diodes with an optimized epitaxial structure are investigated at the wavelength of 980 nm range. The thicknesses of P- and N-waveguides are prudently chosen based on a systematic consideration about mode characteristics and vertical far-field divergences. Broad area laser diodes show a high internal quantum efficiency of 98% and a low internal optical loss of 0.58 cm-1. The ridge-waveguide laser with 7 µm ridge and 3 mm cavity yields 1.9 W single spatial mode output with far-field divergence angles of 6.8° in lateral and 11.5° in vertical at full width at half maximum under 2 A CW operating current. The corresponding M2 values are 1.77 and 1.47 for lateral and vertical, respectively, and the corresponding brightness is 76.8 MW‧cm-2‧sr-1. The far-field divergence angles with 95% power content are in the range of 24.7° to 26.1° across the whole measured range.

Damage Evaluation of Concrete Column under Impact Load Using a Piezoelectric-Based EMI Technique.

One of the major causes of damage to column-supported concrete structures, such as bridges and highways, are collisions from moving vehicles, such as cars and ships. It is essential to quantify the collision damage of the column so that appropriate actions can be taken to prevent catastrophic events. A widely used method to assess structural damage is through the root-mean-square deviation (RMSD) damage index established by the collected data; however, the RMSD index does not truly provide quantitative information about the structure. Conversely, the damage volume ratio that can only be obtained via simulation provides better detail about the level of damage in a structure. Furthermore, as simulation can also provide the RMSD index relating to that particular damage volume ratio, the empirically obtained RMSD index can thus be related to the structural damage degree through comparison of the empirically obtained RMSD index to numerically-obtained RMSD. Thus, this paper presents a novel method in which the impact-induced damage to a structure is simulated in order to obtain the relationship between the damage volume ratio to the RMSD index, and the relationship can be used to predict the true damage degree by comparison to the empirical RMSD index. In this paper, the collision damage of a bridge column by moving vehicles was simulated by using a concrete beam model subjected to continuous impact loadings by a freefalling steel ball. The variation in admittance signals measured by the surface attached lead zirconate titanate (PZT) patches was used to establish the RMSD index. The results demonstrate that the RMSD index and the damage ratio of concrete have a linear relationship for the particular simulation model.

Efficient self-frequency-doubling Nd:GdCOB green laser at 545 nm pumped by a 796 nm laser diode.

A 796 nm laser-diode (LD)-pumped self-frequency-doubling Nd:GdCa4O(BO3)3 (Nd:GdCOB) green laser is first demonstrated. With 2.93 W of 796 nm LD pump power, a maximum power of 460 mW green laser at 545 nm has been achieved. The optical conversion efficiency of 15.8% is higher than that pumped with a 808 nm LD. As the pump wavelength shifts toward 796 nm, the output power and optical conversion efficiency increase.

Graphene-based phononic crystal lenses: Machine learning-assisted analysis and design.

The advance of artificial intelligence and graphene-based composites brings new vitality into the conventional design of acoustic lenses which suffers from high computation cost and difficulties in achieving precise desired refractive indices. This paper presents an efficient and accurate design methodology for graphene-based gradient-index phononic crystal (GGPC) lenses by combing theoretical formulations and machine learning methods. The GGPC lenses consist of two-dimensional phononic crystals possessing square unit cells with graphene-based composite inclusions. The plane wave expansion method is exploited to obtain the dispersion relations of elastic waves in the structures and then establish the data sets of the effective refractive indices in structures with different volume fractions of graphene fillers in composite materials and filling fractions of inclusions. Based on the database established by the theoretical formulation, genetic programming, a superior machine learning algorithm, is introduced to generate explicit mathematical expressions to predict the effective refractive indices under different structural information. The design of GGPC lenses is conducted with the assistance of the machine learning prediction model, and it will be illustrated by several typical design examples. The proposed design method offers high efficiency, accuracy as well as the ability to achieve inverse design of GGPC lenses, thus significantly facilitating the development of novel phononic crystal lenses and acoustic energy focusing.

Vibration and Buckling Characteristics of Functionally Graded Graphene Nanoplatelets Reinforced Composite Beams with Open Edge Cracks.

This paper investigates the free vibration and compressive buckling characteristics of functionally graded graphene nanoplatelets reinforced composite (FG-GPLRC) beams containing open edge cracks by using the finite element method. The beam is a multilayer structure where the weight fraction of graphene nanoplatelets (GPLs) remains constant in each layer but varies along the thickness direction. The effective Young's modulus of each GPLRC layer is determined by the modified Halpin-Tsai micromechanics model while its Poisson's ratio and mass density are predicted according to the rule of mixture. The effects of GPLs distribution pattern, weight fraction, geometry, crack depth ratio (CDR), slenderness ratio as well as boundary conditions on the fundamental frequency and critical buckling load of the FG-GPLRC beam are studied in detail. It was found that distributing more GPLs on the top and bottom surfaces of the cracked FG-GPLRC beam provides the best reinforcing effect for improved vibrational and buckling performance. The fundamental frequency and critical buckling load are also considerably affected by the geometry and dimension of GPL nanofillers.