Acceleration of the Rate of Silver Nanoparticle Formation Using Microbubbles in a Sonochemical Process

The effect of the addition of microbubbles on the formation of silver nanoparticles produced in an ultrasonic radiation-mediated process was investigated. The addition of microbubbles with an area-based median diameter of 26.0?µm and a bubble volume density of 0.18?cm³/L significantly increased the silver nanoparticle formation rate during the sonochemical process. The size distribution of the silver nanoparticles was largely unaffected by the addition of the microbubbles. The influence of changes in the microbubble volume density on the formation of the silver nanoparticles was also investigated; it was confirmed that the rate of formation of the silver nanoparticles increased moderately with increasing volume density. The gradient in absorption spectra was approximately 30 times larger for the case when microbubbles (median diameter: 28.3?µm) were added at 0.74?cm³/L, compared with the case without microbubbles. The results suggested that the microbubbles provided reaction sites similar to cavitation bubbles in the ultrasonic reaction.     

Determination of Thermoelectric Module Efficiency: A Survey

The development of thermoelectrics (TE) for energy conversion is in the transition phase from laboratory research to device development. There is an increasing demand to accurately determine the module efficiency, especially for the power generation mode. For many TE, the figure of merit, ZT, of the material sometimes cannot be fully realized at the device level. Reliable efficiency testing of thermoelectric modules is important to assess the device ZT and provide end-users with realistic values for how much power can be generated under specific conditions. We conducted a general survey of efficiency testing devices and their performance. The results indicated a lack of industry standards and test procedures. This study included a commercial test system and several laboratory systems. Most systems are based on the heat flow meter method, and some are based on the Harman method. They are usually reproducible in evaluating thermoelectric modules. However, different systems often showed large differences that are likely caused by uncertain heat loss and thermal resistance. Efficiency testing is an important capability for the thermoelectric community to improve. A follow-up international standardization effort is planned.     

Optical manipulation of multiple stable states in photorefractive four-wave mixing

Abstract

We propose an approach to manipulate the convergence in multiple solutions of phase conjugate reflectivity in photorefractive four-wave mixing. Although a method forcibly adding a π-phase shift to an incident beam has been already proposed to control the reflectivity, some restrictions have been required in the boundary conditions for the successful operation. Here, we control the reflectivity with the boundary conditions in which the phase shift operation is ineffective by itself. In our method, the phase shift operation is combined with the procedure of turning an incident beam on and off. With a numerical analysis of four-wave mixing, we show that our new approach brings drastic change in the spatial distribution of the index grating and leads the phase conjugate reflectivity which was not manipulated previously.     

Modeling of quantitative relationships between physicochemical properties of active pharmaceutical ingredients and tensile strength of tablets using a boosted tree

Objectives: The aim of this study was to explore the potential of boosted tree (BT) to develop a correlation model between active pharmaceutical ingredient (API) characteristics and a tensile strength (TS) of tablets as critical quality attributes.

Methods: First, we evaluated 81 kinds of API characteristics, such as particle size distribution, bulk density, tapped density, Hausner ratio, moisture content, elastic recovery, molecular weight, and partition coefficient. Next, we prepared tablets containing 50% API, 49% microcrystalline cellulose, and 1% magnesium stearate using direct compression at 6, 8, and 10?kN, and measured TS. Then, we applied BT to our dataset to develop a correlation model. Finally, the constructed BT model was validated using k-fold cross-validation.

Results: Results showed that the BT model achieved high-performance statistics, whereas multiple regression analysis resulted in poor estimations. Sensitivity analysis of the BT model revealed that diameter of powder particles at the 10th percentile of the cumulative percentage size distribution was the most crucial factor for TS. In addition, the influences of moisture content, partition coefficients, and modal diameter were appreciably meaningful factors.

Conclusions: This study demonstrates that BT model could provide comprehensive understanding of the latent structure underlying APIs and TS of tablets.     

Well‐defined,environment‐friendly synthesis of polypeptides based on phosgene‐free transformation of amino acids into urethane derivatives and their applications

In this study, both naturally occurring and artificial amino acids were successfully transformed into the corresponding urethane derivatives using diphenyl carbonate. The urethanes thus prepared could be efficiently cyclized into amino acid N‐carboxyanhydrides (NCAs) without the requirement of phosgene. In addition, the presence of primary amines converted the urethane derivatives into NCAs and initiated the ring‐opening polymerization of the in situ formed NCAs, allowing for the well‐defined synthesis of polypeptides. These polypeptides contained initiating ends functionalized by an amine‐derived residue and propagating ends bearing the reactive amino group. By precise control of the structures of the polypeptides, various polypeptide conjugates such as block copolymers and graft copolymers were successfully synthesized as designed, and their applications in antifouling coatings against proteins, drug delivery systems and biosensors were demonstrated. © 2019 Society of Chemical Industry     

Estimation of anisotropic properties of nano-structured arrays by modal vibration control at microscale

Nano-structured arrays are engineered to meet the requirements of a variety of applications such as microfilters, sensors, and structural interface due to their unique mechanical characteristics, which cannot be achieved by conventional solid materials. However, it is hard to evaluate the elastic properties of nano-structured arrays owing to the discrete structure, sample size, and availability of suitable techniques. To facilitate this, we develop an advanced three-dimensional microscale vibration testing process. In the test, a specially designed three-dimensional microspecimen with tuned mass is excited by a piezoelectric actuator, and the resonance frequencies are detected by a laser device successfully. The anisotropic elastic moduli of nano-structured array composed of helical nano-springs are identified from a single spectrum. This array shows so strong characteristic anisotropy that the solid one hardly can attain. The microscale testing technique can be extended to other materials and microstructures.     

Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data

Fourier-transform infrared (FTIR) spectroscopy is a powerful high-throughput phenotyping tool for predicting traits that are expensive and difficult to measure in dairy cattle. Calibration equations are often developed using standard methods, such as partial least squares (PLS) regression. Methods that employ penalization, rank-reduction, and variable selection, as well as being able to model the nonlinear relations between phenotype and FTIR, might offer improvements in predictive ability and model robustness. This study aimed to compare the predictive ability of 2 machine learning methods, namely random forest (RF) and gradient boosting machine (GBM), and penalized regression against PLS regression for predicting 3 phenotypes differing in terms of biological meaning and relationships with milk composition (i.e., phenotypes measurable directly and not directly in milk, reflecting different biological processes which can be captured using milk spectra) in Holstein-Friesian cattle under 2 cross-validation scenarios. The data set comprised phenotypic information from 471 Holstein-Friesian cows, and 3 target phenotypes were evaluated: (1) body condition score (BCS), (2) blood β-hydroxybutyrate (BHB, mmol/L), and (3) κ-casein expressed as a percentage of nitrogen (κ-CN, % N). The data set was split considering 2 cross-validation scenarios: samples-out random in which the population was randomly split into 10-folds (8-folds for training and 1-fold for validation and testing); and herd/date-out in which the population was randomly assigned to training (70% herd), validation (10%), and testing (20% herd) based on the herd and date in which the samples were collected. The random grid search was performed using the training subset for the hyperparameter optimization and the validation set was used for the generalization of prediction error. The trained model was then used to assess the final prediction in the testing subset. The grid search for penalized regression evidenced that the elastic net (EN) was the best regularization with increase in predictive ability of 5%. The performance of PLS (standard model) was compared against 2 machine learning techniques and penalized regression using 2 cross-validation scenarios. Machine learning methods showed a greater predictive ability for BCS (0.63 for GBM and 0.61 for RF), BHB (0.80 for GBM and 0.79 for RF), and κ-CN (0.81 for GBM and 0.80 for RF) in samples-out cross-validation. Considering a herd/date-out cross-validation these values were 0.58 (GBM and RF) for BCS, 0.73 (GBM and RF) for BHB, and 0.77 (GBM and RF) for κ-CN. The GBM model tended to outperform other methods in predictive ability around 4%, 1%, and 7% for EN, RF, and PLS, respectively. The prediction accuracies of the GBM and RF models were similar, and differed statistically from the PLS model in samples-out random cross-validation. Although, machine learning techniques outperformed PLS in herd/date-out cross-validation, no significant differences were observed in terms of predictive ability due to the large standard deviation observed for predictions. Overall, GBM achieved the highest accuracy of FTIR-based prediction of the different phenotypic traits across the cross-validation scenarios. These results indicate that GBM is a promising method for obtaining more accurate FTIR-based predictions for different phenotypes in dairy cattle.     

Application results of a prototype ultrasonic liquid film sensor to a 7 MPa steam–water two-phase flow experiment

A prototype ultrasonic liquid film sensor was applied to a high-temperature steam–water two-phase flow experiment. The liquid film sensor was vertically installed in a loop which was connected to HUSTLE, a multi-purpose steam source test facility. The hydraulic diameter of the measurement section was 9.4 mm. The output waveforms of the sensor were acquired with a digital oscilloscope. The fluid temperature and system pressure were kept at 288 °C and 7.2 MPa, respectively, during the experiment. The pulse-echo method was used to calculate the liquid film thickness. The cross-correlation calculation was utilized to determine the time difference between the pulse reflected at the sensor surface and the pulse reflected at the liquid film surface. The time-averaged liquid film thicknesses were less than 0.055 mm in the annular flow condition. The increase of the time-averaged thickness was small with the change of the gas momentum flux. The film thicknesses measured with the sensor were compared with the past experimental results; the former were smaller than one-fourth of the thickness estimated as the mean film thickness. The comparison results suggested that the continuous liquid sublayer thickness was measured with the liquid film sensor.     

Thermodynamic Properties of Ionic Semiclathrate Hydrate Formed with Tetrabutylammonium Propionate

The phase equilibrium temperature and dissociation heat of tetrabutylammonium propionate (TBAPr) hydrate are reported. TBAPr hydrate is a type of ionic semiclathrate hydrates and also could potentially be used as thermal energy storage material. The temperature‐composition phase diagram of the TBAPr hydrate was determined in a defined range of mass fractions. Considering the dissociation heat of differential scanning calorimetry (DSC) measurements, multiple peaks of heat flow were observed in the TBAPr‐water system at the TBAPr mass fraction lower than 0.35, and there was a single peak at the mass fraction higher than 0.37.