Triple condensate halo from water droplets impacting on cold surfaces

Understanding the dynamics in the deposition of water droplets onto solid surfaces is of importance from both fundamental and practical viewpoints. While the deposition of a water droplet onto a heated surface is extensively studied, the characteristics of depositing a droplet onto a cold surface and the phenomena leading to such behavior remain elusive. Here we report the formation of a triple condensate halo observed during the deposition of a water droplet onto a cold surface, due to the interplay between droplet impact dynamics and vapor diffusion. Two subsequent condensation stages occur during the droplet spreading and cooling processes, engendering this unique condensate halo with three distinctive bands. We further proposed a scaling model to interpret the size of each band, and the model is validated by the experiments of droplets with different impact velocity and varying substrate temperature. Our experimental and theoretical investigation of the droplet impact dynamics and the associated condensation unravels the mass and heat transfer among droplet, vapor and substrate, offer a new sight for designing of heat exchange devices

Learning to Describe for Predicting Zero-shot Drug-Drug Interactions

Adverse drug-drug interactions~(DDIs) can compromise the effectiveness of concurrent drug administration, posing a significant challenge in healthcare. As the development of new drugs continues, the potential for unknown adverse effects resulting from DDIs becomes a growing concern. Traditional computational methods for DDI prediction may fail to capture interactions for new drugs due to the lack of knowledge. In this paper, we introduce a new problem setup as zero-shot DDI prediction that deals with the case of new drugs. Leveraging textual information from online databases like DrugBank and PubChem, we propose an innovative approach TextDDI with a language model-based DDI predictor and a reinforcement learning~(RL)-based information selector, enabling the selection of concise and pertinent text for accurate DDI prediction on new drugs. Empirical results show the benefits of the proposed approach on several settings including zero-shot and few-shot DDI prediction, and the selected texts are semantically relevant. Our code and data are available at \url{https://github.com/zhufq00/DDIs-Prediction}

A Diffusion Model for Event Skeleton Generation

Event skeleton generation, aiming to induce an event schema skeleton graph with abstracted event nodes and their temporal relations from a set of event instance graphs, is a critical step in the temporal complex event schema induction task. Existing methods effectively address this task from a graph generation perspective but suffer from noise-sensitive and error accumulation, e.g., the inability to correct errors while generating schema. We, therefore, propose a novel Diffusion Event Graph Model~(DEGM) to address these issues. Our DEGM is the first workable diffusion model for event skeleton generation, where the embedding and rounding techniques with a custom edge-based loss are introduced to transform a discrete event graph into learnable latent representation. Furthermore, we propose a denoising training process to maintain the model's robustness. Consequently, DEGM derives the final schema, where error correction is guaranteed by iteratively refining the latent representation during the schema generation process. Experimental results on three IED bombing datasets demonstrate that our DEGM achieves better results than other state-of-the-art baselines. Our code and data are available at https://github.com/zhufq00/EventSkeletonGeneration

A Generative Approach for Script Event Prediction via Contrastive Fine-tuning

Script event prediction aims to predict the subsequent event given the context. This requires the capability to infer the correlations between events. Recent works have attempted to improve event correlation reasoning by using pretrained language models and incorporating external knowledge~(e.g., discourse relations). Though promising results have been achieved, some challenges still remain. First, the pretrained language models adopted by current works ignore event-level knowledge, resulting in an inability to capture the correlations between events well. Second, modeling correlations between events with discourse relations is limited because it can only capture explicit correlations between events with discourse markers, and cannot capture many implicit correlations. To this end, we propose a novel generative approach for this task, in which a pretrained language model is fine-tuned with an event-centric pretraining objective and predicts the next event within a generative paradigm. Specifically, we first introduce a novel event-level blank infilling strategy as the learning objective to inject event-level knowledge into the pretrained language model, and then design a likelihood-based contrastive loss for fine-tuning the generative model. Instead of using an additional prediction layer, we perform prediction by using sequence likelihoods generated by the generative model. Our approach models correlations between events in a soft way without any external knowledge. The likelihood-based prediction eliminates the need to use additional networks to make predictions and is somewhat interpretable since it scores each word in the event. Experimental results on the multi-choice narrative cloze~(MCNC) task demonstrate that our approach achieves better results than other state-of-the-art baselines. Our code will be available at https://github.com/zhufq00/mcnc

Electronic Delocalization Engineering of β‐AsP Enabled High‐Efficient Multisource Logic Nanodevices

Delocalized electron and phonon structures are directives for rationally tuning the intrinsic physicochemical properties of 2D materials by redistributing electronic density. However, it is still challenging to accurately manipulate the delocalized electron and systematically study the relationships between physiochemical properties and practical nanodevices. Herein, the effects of delocalized electrons engineering on blue-arsenic-phosphorus (β-AsP)-based practical devices are systematically investigated via implementing vacancies or heteroatom doping. A tendency of carrier conductivity property from “half-metal” to “metal” is initially found when tuning the electronic structure of β-AsP with adjustable vacancy concentrations below 2 at% or above 3 at%, which can be ascribed to the introduction of delocalized electrons that cause asymmetric contributions to the electronic states near the implementation site. In optical logic device simulations, broadband response, triangular wave circuit system signal, and reverse polarization anisotropy are achieved by adjusting the vacancy concentration, while extinction ratios are as high as 1561. The electric and thermic-logic devices realize the highest available reported giant magnetoresistance (MR) up to 1013% and 1039% at vacancy concentrations of 1.67% and 0.89%, respectively, which is significantly superior to the reports. The results shed light on the electronic delocalization strategy of regulating internal structures to achieve highly efficient nanodevices

Unconventional inorganic characteristics of 4 types of Xinjiang coals and their influence on the generation of ultrafine particles

A deep understanding of the effects of the inorganic characteristics of Xinjiang coals on combustion particulate generation is of great significance for their clean and efficient utilization. This work investigated the inorganic characteristics of Wucaiwan coal (WCW), Wanxiang coal (WX), Tianchi coal (TC), and Xiheishan coal (XHS) and the their relationship with the production of ultrafine particulates during combustion. The results show that the four Xinjiang coals are mainly lignites with low ash and sulfur contents. Coal ashes are enriched in basic elements. Among them, the content of Na2O (3.58%−7.13%) is commonly higher than that of conventional utility coals. The ashes of WCW and WX coals have higher contents of CaO (> 33%), but low contents of SiO2 and Al2O3. WX coal is particularly characterized by high Na and Cl. The Na in coal is primarily water soluble(62.3%−90.6%). The K is mainly HCl insoluble. The Fe occurs primarily as HCl soluble and insoluble forms. And the distribution of Ca and Mg in different forms varies according to coal types. The composition of ultrafine particulate matter from Xinjiang coal combustion is dominated by Na, K, Cl, and S. The particle size range of the ultrafine particulate matter is accurately defined by using the condition that the mass fraction of Na2O+K2O+Cl+SO3 is higher than 50%. The ultrafine particulate matter of WCW, TC and XHS coals has similar particle size ranges (≤0.07 μm); while the ultrafine particulate matter of WX coal, which has the highest content of Na in the water-soluble form, has a wider particle size range (≤0.76 μm). The generation of ultrafine particulate matter (y) is found to be highly linearly and positively correlated with the total amount of water-soluble (Na+K) (x) in the coal, with the relationship equation y = 0.528x−0.239 and the correlation coefficient of 0.948

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Condensation frosting and freezing of impact water droplets on cold surfaces

The accretion of ice/frost stemming from condensation frosting and freezing of impact water droplets is pervasive in both nature and industry, causing enormous losses every year worldwide especially in aviation and transportation. Most existing studies regarding condensation frosting focus on the anti-frosting potential by leveraging the ice-liquid or ice-vapor interactions on two-dimensional structured surfaces. Little is known about condensation frosting on one-dimensional surfaces with temperature gradient. This thesis first reports on the non-uniform condensate morphologies on a cantilevered microfiber during condensation due to the competition between conductive thermal resistance within the fiber and condensation heat transfer resistance on the fiber surface. Scaling analyses were provided to reveal the underlying physics. During the frosting phase, the nucleation of supercooled liquid microdroplet is triggered at locations where ice bridges are upon contact instead droplet-substrate interfaces. Furthermore, an inter-droplet ice wicking regime is reported where the liquid droplet is sucked by physical contact of ice bridge, different from the classical inter-droplet ice bridging. Such phenomenon results from the competition between wicking dynamics and nucleation crystallization timescales. Intriguingly, the frosting morphology on the microfiber demonstrates a similar trend to the condensate morphology. Moreover, the directional migration and easy assembling of melted water can be achieved by tailoring the fiber length and cooling temperature during defrosting, and well explained by the analytical models proposed. Additionally, a unique distribution pattern of condensed droplets is found during condensation on stainless-steel mesh, with one droplet atop every other knot. Such phenomenon arises from the extent of the region of inhibited condensation imposed by the central knot within the smallest periodic unit of the distribution pattern. Lattice Boltzmann simulation is implemented to obtain the water vapor concentration, and the pattern region under various ambient temperature and mesh specifications is predicted based on a proposed model validated with experimental data. The dynamics of water droplets impacting on isothermal surfaces (with the same temperature as water) have been extensively studied over the years, and so have the freezing dynamics of sessile droplets on cold substrates. However, the underlying mechanisms of freezing of impact water droplets on cold surfaces remain elusive. This thesis then focuses on the impact behavior of water droplets on cold surfaces. A triple condensate halo is found during a water droplet impacting at low velocity upon a cold surface. Due to the interaction of droplet impact and vapor mass diffusion during droplet spreading and cooling, two condensation stages occur, engendering this unique condensate halo with three distinctive bands. Furthermore, five different freezing morphologies of impact droplets were discovered when room-temperature water droplets impacted perpendicularly on a sufficiently cooled superhydrophilic surface, depending upon the impact velocity and substrate temperature. The formation of such morphologies results from the competition between the timescales associated with droplet solidification heat transfer and impact hydrodynamics. A phase diagram is developed based on scaling analyses to demonstrate how the freezing morphologies are governed by droplet impact and solidification related timescales which well interprets the experimental findings. For inclined surfaces, another four different freezing morphologies are found which can be partially explained by modifying the scaling analyses applied in the perpendicular impact. Quite different from the cases on hydrophilic surfaces, the frozen impacted droplets can self-peel completely and become easily removable from a hydrophobic surface sufficiently cooled. The intriguing phenomena are rationalized by comparing the strength of thermal contraction of ice, ice-ice cohesion, and ice-substrate adhesion. In addition, a model is developed to characterize the peeling and bending of the frozen impacted water droplet, which can qualitatively explain the experimental observations.Doctor of Philosoph

Self-peeling of frozen water droplets upon impacting a cold surface

Freezing of water droplets impacting a cold substrate is a commonly encountered circumstance impairing the performance and safety of various applications. Active methods of ice removal such as heating or mechanical means are energy intensive and inconvenient. Here, we report a passive ice removal method via harvesting the thermal-mechanical stress of ice, leading to the self-peeling of frozen water droplets upon impacting a cold substrate. We find that the frozen ice completely self-peels and is then easily removable from a cold hydrophobic surface whiles the ice exhibits cracking and remains firmly sticky to a hydrophilic surface. The peeling behaviors of frozen water droplets are then scrutinized by varying the subcooling degree, impact parameters and wettability. Moreover, we develop a theoretical model to characterize the peeling and bending behaviors of the ice and also provides a simple criterion to predict the occurrence of complete self-peeling, facilitating the design of anti-icing surfaces.Ministry of Education (MOE)Published versionThis work was supported by National Postdoctoral Program for Innovative Talents (No. BX2021235) and the Ministry of Education of Singapore via Tier 2 Academic Research Fund (MOE2016-T2-1-114)(awarded to C.Y.). W.Z.F. thanks the fund supported by Key Laboratory of Icing and Anti/De-icing of CARDC (Grant No. IADL20210105). L.Z. thanks the start-up grant (Grant No. R-265-000-696-133) given by the National University of Singapore

How different freezing morphologies of impacting droplets form

Freezing morphologies of impacting water droplets depend on the interaction between droplet spreading and solidification. The existing studies showed that the shape of frozen droplets mostly is of spherical cap with a singular tip, because of much shorter timescale of the droplet spreading than that of the solidification. Here, we create the experimental conditions of extended droplet spreading and greatly enhanced heat transfer for fast solidification, thereby allowing to study such droplet freezing process under the strong coupling of the droplet spreading and solidification.Ministry of Education (MOE)This work was supported by the Ministry of Education of Singapore via Tier 2 Academic Research Fund (MOE2016- T2-1-114)