Computational Study of Drop-on-Demand Coaxial Electrohydrodynamic Jet and Printing Microdroplets.

Currently, coaxial electrohydrodynamic jet (CE-Jet) printing is used as a promising technique for the alternative fabrication of drop-on-demand micro- and nanoscale structures without using a template. Therefore, this paper presents numerical simulation of the DoD CE-Jet process based on a phase field model. Titanium lead zirconate (PZT) and silicone oil were used to verify the numerical simulation and the experiments. The optimized working parameters (i.e., inner liquid flow velocity 150 m/s, pulse voltage 8.0 kV, external fluid velocity 250 m/s, print height 16 cm) were used to control the stability of the CE-Jet, avoiding the bulging effect during experimental study. Consequently, different sized microdroplets with a minimum diameter of ~5.5 µm were directly printed after the removal of the outer solution. The model is considered the easiest to implement and is powerful for the application of flexible printed electronics in advanced manufacturing technology.

High-performance flexible organic field effect transistors with print-based nanowires.

Polymer nanowire (NW) organic field-effect transistors (OFETs) integrated on highly aligned large-area flexible substrates are candidate structures for the development of high-performance flexible electronics. This work presents a universal technique, coaxial focused electrohydrodynamic jet (CFEJ) printing technology, to fabricate highly aligned 90-nm-diameter polymer arrays. This method allows for the preparation of uniformly shaped and precisely positioned nanowires directly on flexible substrates without transfer, thus ensuring their electrical properties. Using indacenodithiophene-co-benzothiadiazole (IDT-BT) and poly(9,9-dioctylfluorene-co-benzothiadiazole) (F8-BT) as example materials, 5 cm2 arrays were prepared with only minute size variations, which is extremely difficult to do using previously reported methods. According to 2D-GIXRD analysis, the molecules inside the nanowires mainly adopted face-on π-stacking crystallite arrangements. This is quite different from the mixed arrangement of thin films. Nanowire-based OFETs exhibited a high average hole mobility of 1.1 cm2 V-1 s-1 and good device uniformity, indicating the applicability of CFEJ printing as a potential batch manufacturing and integration process for high-performance, scalable polymer nanowire-based OFET circuits. This technique can be used to fabricate various polymer arrays, enabling the use of organic polymer semiconductors in large-area, high-performance electronic devices and providing a new path for the fabrication of flexible displays and wearable electronics in the future.

Optimized coaxial focused electrohydrodynamic jet printing of highly ordered semiconductor sub-microwire arrays for high-performance organic field-effect transistors.

Patterning of semiconductor polymers is pertinent to preparing and applying organic field-effect transistors (OFETs). In this study, coaxial focused electrohydrodynamic jet printing (high resolution, high speed, and convenient) was used to pattern polymer semiconductors. The influence of the key printing parameters on the width of polymer sub-microwires was evaluated. The width decreased with increasing applied voltage, printing speed, and concentration of the polymer ink. However, the width increased gradually with increasing polymer ink flow rate. A regression analysis model of the relationship between the printing parameters and width was established. Based on a regression analysis/genetic algorithm, the optimal printing parameters were obtained and the correctness of the printing parameters was verified. The optimized printing parameters stabilized the width of the arrays to ca. 110 nm and imparted a smooth morphology. Additionally, the corresponding OFETs exhibited a high mobility of 2 cm2 V-1 s-1, which is 5× higher than that of thin-film-based OFETs. One can conveniently obtain high-performance OFETs from ordered sub-microwire arrays fabricated by CFEJ printing.

Subtypes specified environmental dependence of seasonal influenza virus.

Understanding the role of meteorological factors in the transmission dynamics of respiratory infectious diseases remains challenging. Our study was to comprehensively investigate the nonlinear effects of environmental factors on influenza transmission, based on multi-region surveillance data from mainland China. An approach related to time-varying reproduction number (Rt) was proposed, which extracts the environment-related components from Rt to estimate the relationship between environmental factors and influenza transmission based on a mixed-effects regression model. Nonlinear relationships for absolute humidity (the lowest transmission was observed at absolute humidity of 12 g/m3) and mean temperature (the lowest transmission was observed at the mean temperature of 18 °C) with influenza transmission were observed. Influenza transmission holds almost constant with the average precipitation below 1 mm or sunshine hour below 9 h/day, but increases for the precipitation and decreases for the sunshine hour afterward. The environmental dependence varies across subtypes: A(H3N2) maintains relatively higher transmission in high temperature and humidity conditions, compared with other influenza subtypes. Overall, the subtypes specified environmental dependence of influenza transmission could explain 23.1 %, 29.2 % and 27.1 % of the variations for A(H1N1)pdm09, A(H3N2) and B-lineage in China. The projected seasonal transmission rates based on our approach could be used as a valuable seasonal proxy to model the influenza dynamics under various meteorological spaces. Finally, our approach is also applicable to obtain novel insights into the impact of environmental factors on other respiratory infectious diseases.

Simulation of Cone-Jet and Micro-Drip Regimes and Printing of Micro-Scale Patterns on PET Substrate.

The fabrication of various micro-patterns on polymer insulating substrates is a current requirement in micro-electromechanical system (MEMS) and packaging sectors. In this paper, we use electrohydrodynamic jet (E-Jet) printing to create multifaceted and stable micro-patterns on a polyethylene terephthalate (PET) substrate. Initially, simulation was performed to investigate optimized printing settings in phase field physics for the usage of two distinct functional inks. A series of simulation experiments was conducted, and it was determined that the following parameters are optimised: applied pressure of 40 kPa, high pulse voltage of 1.95 kV, low dc voltage of 1.60 kV, duty cycle of 80%, pulse frequency of 60 Hz, printing height of 0.25 mm, and printing speed of 1 mm/s. Then, experiments showed that adjusting a pressure value of 40 kPa and regulating the SEMICOSIL988/1 K ink to print micro-drops on a polymer substrate with a thickness of 1 mm prevents coffee staining. The smallest measured droplet size was 200 µm. Furthermore, underfill (UF 3808) ink was driven with applied pressure to 50 kPa while other parameters were left constant, and the minimum size of linear patterns was printed to 105 µm on 0.5-mm-thick PET substrate. During the micro-drip and cone-jet regimes, the consistency and diameter of printed micro-structures were accurately regulated at a pulse frequency of 60 Hz and a duty cycle of 80%.

Drivers and forecasts of multiple waves of the coronavirus disease 2019 pandemic: A systematic analysis based on an interpretable machine learning framework.

Coronavirus disease 2019 (COVID-19) has become a global pandemic and continues to prevail with multiple rebound waves in many countries. The driving factors for the spread of COVID-19 and their quantitative contributions, especially to rebound waves, are not well studied. Multidimensional time-series data, including policy, travel, medical, socioeconomic, environmental, mutant and vaccine-related data, were collected from 39 countries up to 30 June 2021, and an interpretable machine learning framework (XGBoost model with Shapley Additive explanation interpretation) was used to systematically analyze the effect of multiple factors on the spread of COVID-19, using the daily effective reproduction number as an indicator. Based on a model of the pre-vaccine era, policy-related factors were shown to be the main drivers of the spread of COVID-19, with a contribution of 60.81%. In the post-vaccine era, the contribution of policy-related factors decreased to 28.34%, accompanied by an increase in the contribution of travel-related factors, such as domestic flights, and contributions emerged for mutant-related (16.49%) and vaccine-related (7.06%) factors. For single-peak countries, the dominant ones were policy-related factors during both the rising and fading stages, with overall contributions of 33.7% and 37.7%, respectively. For double-peak countries, factors from the rebound stage contributed 45.8% and policy-related factors showed the greatest contribution in both the rebound (32.6%) and fading (25.0%) stages. For multiple-peak countries, the Delta variant, domestic flights (current month) and the daily vaccination population are the three greatest contributors (8.12%, 7.59% and 7.26%, respectively). Forecasting models to predict the rebound risk were built based on these findings, with accuracies of 0.78 and 0.81 for the pre- and post-vaccine eras, respectively. These findings quantitatively demonstrate the systematic drivers of the spread of COVID-19, and the framework proposed in this study will facilitate the targeted prevention and control of the ongoing COVID-19 pandemic.

Synchronized nonpharmaceutical interventions for the control of COVID-19.

The world is experiencing an ongoing pandemic of coronavirus disease-2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In attempts to control the pandemic, a range of nonpharmaceutical interventions (NPIs) has been implemented worldwide. However, the effect of synchronized NPIs for the control of COVID-19 at temporal and spatial scales has not been well studied. Therefore, a meta-population model that incorporates essential nonlinear processes was constructed to uncover the transmission characteristics of SARS-CoV-2 and then assess the effectiveness of synchronized NPIs on COVID-19 dynamics in China. Regional synchronization of NPIs was observed in China, and it was found that a combination of synchronized NPIs (the travel restrictions, the social distancing and the infection isolation) prevented 93.7% of SARS-CoV-2 infections. The use of synchronized NPIs at the time of the Wuhan lockdown may have prevented as much as 38% of SARS-CoV-2 infections, compared with the unsynchronized scenario. The interconnectivity of the epicenter, the implementation time of synchronized NPIs, and the number of regions considered all affected the performance of synchronized NPIs. The results highlight the importance of using synchronized NPIs in high-risk regions for the control of COVID-19 and shed light on effective strategies for future pandemic responses. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11071-021-06505-0.

A Mutation Network Method for Transmission Analysis of Human Influenza H3N2.

Characterizing the spatial transmission pattern is critical for better surveillance and control of human influenza. Here, we propose a mutation network framework that utilizes network theory to study the transmission of human influenza H3N2. On the basis of the mutation network, the transmission analysis captured the circulation pattern from a global simulation of human influenza H3N2. Furthermore, this method was applied to explore, in detail, the transmission patterns within Europe, the United States, and China, revealing the regional spread of human influenza H3N2. The mutation network framework proposed here could facilitate the understanding, surveillance, and control of other infectious diseases.