Investigating measurements of fine particle (PM2.5) emissions from the cooking of meals and mitigating exposure using a cooker hood

There is growing awareness that indoor exposure to particulate matter with diameter ≤ 2.5 μm (PM_2.5) is associated with an increased risk of adverse health effects. Cooking is a key indoor source of PM_2.5 and an activity conducted daily in most homes. Population scale models can predict occupant exposures to PM_2.5, but these predictions are sensitive to the emission rates used. Reported emission rates are highly variable and are typically for the cooking of single ingredients and not full meals. Accordingly, there is a need to assess PM_2.5 emissions from the cooking of complete meals. Mean PM_2.5 emission rates and source strengths were measured for four complete meals. Temporal PM_2.5 concentrations and particle size distributions were recorded using an optical particle counter (OPC), and gravimetric sampling was used to determine calibration factors. Mean emission rates and source strengths varied between 0.54—3.7 mg/min and 15—68 mg, respectively, with 95% confidence. Using a cooker hood (apparent capture efficiency > 90%) and frying in non‐stick pans were found to significantly reduce emissions. OPC calibration factors varied between 1.5 and 5.0 showing that a single value cannot be used for all meals and that gravimetric sampling is necessary when measuring PM_2.5 concentrations in kitchens.     

Enthalpy- versus Entropy-Driven Molecular Recognition in the Era of Biologics

Our laboratory has recently identified two nanobodies (small antibodies produced by camelids)—Nb1 and Nb6—that bind efficiently to epithelial growth factor (EGF) and inhibit its ability to activate its receptor (EGFR). Because of the relevance of the EGF/EGFR axis as a target in oncology, these new nanobodies have promising therapeutic potential. This article, however, is focused on another feature of these nanobodies: their distinct thermodynamic signatures. Nb1 binds to EGF through an entropy-driven mechanism whereas Nb6 binds to this factor under enthalpic control. We discuss the advantages and disadvantages of each mechanism in the contexts of traditional medical chemistry (small-molecule drugs) and also of biological drugs. In this latter case, the implications in terms of selectivity are far from being clearly established and further experimental data are required. Their monomeric natures, high stability, and ease of recombinant production make nanobodies ideally suited for thermodynamic studies. Moreover, nanobodies, thanks to their simpler structures in comparison with conventional antibodies, might provide better understanding of the structural basis of the thermodynamic parameters of antigen recognition.     

Single image dehazing using a new color channel

Images with hazy scene suffer from low-contrast, which reduces the visible quality of the scene, thus making object detection a more challenging task. Low-contrast can result from foggy weather conditions during image acquisition. Dehazing is a process of removal of haze from the photography of a hazy scene. Single-image dehazing based on dark channel priors are well-known techniques in this field. However, the performance of such techniques is limited to priors or constraints. Moreover, this type of method fails when images have sky-region. So, a method is proposed, which can restore the visibility of hazy images. First, a hazy image is divided into blocks of size 32 × 32, then the score of each block is calculated to select a block having the highest score. Atmospheric light is calculated from the selected block. A new color channel is considered to remove atmospheric scattering, obtained channel value and atmospheric light are then used to calculate the transmission map in the second step. Third, radiance is computed using a transmission map and atmospheric light. The illumination scaling factor is adopted to enhance the quality of a dehazed image in the final step. Experiments are performed on six datasets namely, I-HAZE, O-HAZE, BSDS500, FRIDA, RESIDE dataset and natural images from Google. The proposed method is compared against 11 state-of-the-art methods. The performance is analyzed using fourteen quantitative evaluation metrics. All the results demonstrate that the proposed method outperforms 11 state-of-the-art methods in most of the cases.     

Nacre-Inspired,Liquid Metal-Based Ultrasensitive Electronic Skin by Spatially Regulated Cracking Strategy

The realization of liquid metal-based wearable systems will be a milestone toward high-performance, integrated electronic skin. However, despite the revolutionary progress achieved in many other components of electronic skin, liquid metal-based flexible sensors still suffer from poor sensitivity due to the insufficient resistance change of liquid metal to deformation. Herein, a nacre-inspired architecture composed of a biphasic pattern (liquid metal with Cr/Cu underlayer) as “bricks” and strain-sensitive Ag film as “mortar” is developed, which breaks the long-standing sensitivity bottleneck of liquid metal-based electronic skin. With 2 orders of magnitude of sensitivity amplification while maintaining wide (>85%) working range, for the first time, liquid metal-based strain sensors rival the state-of-art counterparts. This liquid metal composite features spatially regulated cracking behavior. On the one hand, hard Cr cells locally modulate the strain distribution, which avoids premature cut-through cracks and prolongs the defect propagation in the adjacent Ag film. On the other hand, the separated liquid metal cells prevent unfavorable continuous liquid-metal paths and create crack-free regions during strain. Demonstrated in diverse scenarios, the proposed design concept may spark more applications of ultrasensitive liquid metal-based electronic skins, and reveals a pathway for sensor development via crack engineering.     

Magnetic Resonance Signal Amplification by Reversible Exchange of Selective PyFALGEA Oligopeptide Ligands Towards Epidermal Growth Factor Receptors

The biorelevant PyFALGEA oligopeptide ligand, which is selective towards the epidermal growth factor receptor (EGFR), has been successfully employed as a substrate in magnetic resonance signal amplification by reversible exchange (SABRE) experiments. It is demonstrated that PyFALGEA and the iridium catalyst IMes form a PyFALGEA:IMes molecular complex. The interaction between PyFALGEA:IMes and H₂ results in a ternary SABRE complex. Selective 1D EXSY experiments reveal that this complex is labile, which is an essential condition for successful hyperpolarization by SABRE. Polarization transfer from parahydrogen to PyFALGEA is observed leading to significant enhancement of the ¹H NMR signals of PyFALGEA. Different iridium catalysts and peptides are inspected to discuss the influence of their molecular structures on the efficiency of hyperpolarization. It is observed that PyFALGEA oligopeptide hyperpolarization is more efficient when an iridium catalyst with a sterically less demanding NHC ligand system such as IMesBn is employed. Experiments with shorter analogues of PyFALGEA, that is, PyLGEA and PyEA, show that the bulky phenylalanine from the PyFALGEA oligopeptide causes steric hindrance in the SABRE complex, which hampers hyperpolarization with IMes. Finally, a single-scan ¹H NMR SABRE experiment of PyFALGEA with IMesBn revealed a unique pattern of NMR lines in the hydride region, which can be treated as a fingerprint of this important oligopeptide.     

Degradation pattern of contact resistance and characteristic trap energy in blue organic light-emitting diodes

Thin and lightweight organic light-emitting diodes (OLEDs) are promising candidates for next-generation rollable displays; they offer numerous advantages, such as scalable manufacturing, high color contrast ratio, flexibility, and wide viewing angle. Despite the numerous merits of OLEDs, the insufficient lifetime and stability of blue OLEDs remain unresolved, thereby necessitating a feedback strategy for lifetime extension. Herein, we propose a simple yet effective methodology to determine the contact resistance (R_CT) and characteristic trap energy (E_T) of OLEDs simultaneously in the trapped-charge-limited-conduction regime, where electroluminescence occurs primarily. To validate our approach, the extracted R_CT and E_T values are directly compared with each other by connecting a commercial resistor (R_C) to a blue OLED in series. The percent errors discovered in R_C and E_T are less than 7% and 4%, demonstrating the high feasibility and accuracy of our approach. We further employ this method to study the degradation mechanism of a blue OLED by presenting the electrical stress time- and cycle-dependent R_CT, E_T, ideality factor, and turn-on voltage, revealing different degradation patterns of the metal-to-transport layer interface and emission layer, respectively. Our results provide better insights into the electrical parameter extraction method and electrical current degradation mechanism in blue OLEDs.     

The validation and further development of a multidimensional cognitive load scale for virtual environments

Measuring cognitive load is important in virtual learning environments (VLE). Thus, valid and reliable measures of cognitive load are important to support instructional design in VLE. Through three studies, we investigated the validity and reliability of Leppink's Cognitive Load Scale (CLS) and developed the extraneous cognitive load (EL) dimension into three sub-scales relevant for VLE: EL instructions, EL interaction, and EL environment. We investigated the validity of the measures using the Partial Credit Model (PCM), Confirmatory Factor Analysis (CFA), and correlations with retention tests. Study 1 (n = 73) investigated the adapted version of the CLS. Study 2 describes the development and validation of the Multidimensional Cognitive Load Scale for Virtual Environments (MCLSVE), with 140 students in higher education. Study 3 tested the generalizability of the results with 121 higher education students in a more complicated VLE. The results provide initial evidence for the validity and reliability of the MCLSVE.     

Prediction of mode I fracture toughness of rock using linear multiple regression and gene expression programming

Prediction of mode I fracture toughness (K_IC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression (LMR) and gene expression programming (GEP) methods were used to provide a reliable relationship to determine mode I fracture toughness of rock. The presented model was developed based on 60 datasets taken from the previous literature. To predict fracture parameters, three mechanical parameters of rock mass including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), and elastic modulus (E) have been selected as the input parameters. A cluster of data was collected and divided into two random groups of training and testing datasets. Then, different statistical linear and artificial intelligence based nonlinear analyses were conducted on the training data to provide a reliable prediction model of K_IC. These two predictive methods were then evaluated based on the testing data. To evaluate the efficiency of the proposed models for predicting the mode I fracture toughness of rock, various statistical indices including coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) were utilized herein. In the case of testing datasets, the values of R², RMSE, and MAE for the GEP model were 0.87, 0.188, and 0.156, respectively, while they were 0.74, 0.473, and 0.223, respectively, for the LMR model. The results indicated that the selected GEP model delivered superior performance with a higher R² value and lower errors.     

响应面法优化农产品中抗性淀粉的体外检测

目的 采用响应面法优化农产品中抗性淀粉(Resistant Starch, RS)的体外检测方法。方法 选择谷类、薯类、豆类3类抗性淀粉主要来源的农产品，设计单因素试验和Box-Behnken试验。单因素试验考察了水浴时间、淀粉葡萄糖苷酶（AMYLOGLUCOSIDASE, AMG）作用温度、AMG作用时间、AMG添加量和离心力与离心时间5个条件，并选择AMG作用温度、AMG作用时间、AMG添加量来设计Box-Behnken试验。结果 根据单因素试验，确定了水浴时间为14 h，离心条件选择4500 g离心5 min；根据响应面实验确定对RS含量检测结果影响程度排序为AMG添加量＞AMG作用时间＞AMG作用温度。结合单因素响应面试验，确定最佳检测方法为水浴14 h，4500 g离心5 min，AMG作用温度49℃、AMG添加量0.2 mL、AMG作用时间38 min。结论 优化后的方法重复性良好，适用于玉米、马铃薯和红豆3类样品，且有望适用于其他主要农产品。