Molecular Mechanisms and Design of Hydrogen-Bonded Materials for Thermal Applications

Heat transfer at the nanoscale plays an important role in determining the reliability and performance of many innovative advanced materials technologies such as nanoelectronics, semiconductor, biomedical devices, polymers, and composites. Extensive efforts have been made to design materials with extraordinary thermal properties. However, fundamental understanding of heat transfer in many of these materials is still not lacking, because the thermal transport processes are governed by several factors including molecular morphology and chemical bonding. Among these factors, the atomic bonding between two dissimilar materials or within single materials is of particular interest due to its ubiquity and importance in physical processes. This work will focus on the demonstration and fundamental understanding of nanoscale thermal transport enhanced by incorporating hydrogen bonds in materials design. Molecular dynamics is performed for studying heat transfer processes in two typical hydrogen-bonded materials: (1) protein secondary structures, and (2) electrode/electrolyte composites in lithium ion batteries. Theoretical calculation and analysis show that heat transfer can be tuned in a wide range by modifying the hydrogen bonds. Results will not only provide new physical insights, but will also guide the rational design of materials for desired thermal properties towards many applications

Electronic structures of organic molecule encapsulated BN nanotubes under transverse electric field

The electronic structures of boron nitride nanotubes (BNNTs) doped by different organic molecules under a transverse electric field were investigated via first-principles calculations. The external field reduces the energy gap of BNNT, thus makes the molecular bands closer to the BNNT band edges and enhances the charge transfers between BNNT and molecules. The effects of the electric field direction on the band structure are negligible. The electric field shielding effect of BNNT to the inside organic molecules is discussed. Organic molecule doping strongly modifies the optical property of BNNT, and the absorption edge is red-shifted under static transverse electric field.Comment: accepted by JC

A first principles study on organic molecules encapsulated BN nanotubes

The electronic structures of boron nitride nanotubes (BNNTs) doped by organic molecules are investigated with density functional theory. Electrophilic molecule introduces acceptor states in the wide gap of BNNT close to the valence band edge, which makes the doped system a $p$-type semiconductor. However, with typical nucleophilic organic molecules encapsulation, only deep occupied molecular states but no shallow donor states are observed. There is a significant electron transfer from BNNT to electrophilic molecule, while the charge transfer between nucleophilic molecule and BNNT is neglectable. When both electrophilic and nucleophilic molecules are encapsulated in the same BNNT, large charge transfer between the two kinds of molecules occurs. The resulted small energy gap can strongly modify the transport and optical properties of the system

Masked Vision and Language Pre-training with Unimodal and Multimodal Contrastive Losses for Medical Visual Question Answering

Medical visual question answering (VQA) is a challenging task that requires answering clinical questions of a given medical image, by taking consider of both visual and language information. However, due to the small scale of training data for medical VQA, pre-training fine-tuning paradigms have been a commonly used solution to improve model generalization performance. In this paper, we present a novel self-supervised approach that learns unimodal and multimodal feature representations of input images and text using medical image caption datasets, by leveraging both unimodal and multimodal contrastive losses, along with masked language modeling and image text matching as pretraining objectives. The pre-trained model is then transferred to downstream medical VQA tasks. The proposed approach achieves state-of-the-art (SOTA) performance on three publicly available medical VQA datasets with significant accuracy improvements of 2.2%, 14.7%, and 1.7% respectively. Besides, we conduct a comprehensive analysis to validate the effectiveness of different components of the approach and study different pre-training settings. Our codes and models are available at https://github.com/pengfeiliHEU/MUMC.Comment: accepted by MICCAI202

Measurement report: The promotion of low-level jet and thermal-effect on development of deep convective boundary layer at the southern edge of the Taklimakan Desert

A vigorous development process of the deep convective boundary layer (CBL) was observed at the southern edge of the Taklimakan Desert on 6 June, 2022. Based on coherent Doppler wind lidar and ERA5 data, the formation mechanism of the deep CBL exceeding 5 km was well analyzed, which was mainly promoted by the low-level jet (LLJ) and thermal-effect. The LLJ has made sufficient momentum, energy and material preparations for the development of the deep CBL. Firstly, the cold downhill airflow of the Tibet Plateau leading to LLJ weakens the height and intensity of the temperature inversion layer, which reduces the energy demand for the broken of the IL. Secondly, the LLJ not only supplements the material and energy in the residual layer, but also suppresses the exchange with the lower atmosphere. In addition, the LLJ provides a driving force for the development of the deep CBL. In terms of thermal factors, the Tibet Plateau sensible heat driven air-pump and cold front transit provide additional impetus for the development of the deep CBL. Finally, the formation of deep CBL was catalyzed by the extreme thermal effects of the underlying surface, such as the furnace effect and the atmospheric superadiabatic expansion process. The study of the development of the deep CBL is important for revealing the land-air exchange process of momentum, energy, and material between the Taklimakan Desert and the Tibetan Plateau

A Decision Procedure for Path Feasibility of String Manipulating Programs with Integer Data Type

Strings are widely used in programs, especially in web applications. Integer data type occurs naturally in string-manipulating programs, and is frequently used to refer to lengths of, or positions in, strings. Analysis and testing of string-manipulating programs can be formulated as the path feasibility problem: given a symbolic execution path, does there exist an assignment to the inputs that yields a concrete execution that realizes this path? Such a problem can naturally be reformulated as a string constraint solving problem. Although state-of-the-art string constraint solvers usually provide support for both string and integer data types, they mainly resort to heuristics without completeness guarantees. In this paper, we propose a decision procedure for a class of string-manipulating programs which includes not only a wide range of string operations such as concatenation, replaceAll, reverse, and finite transducers, but also those involving the integer data-type such as length, indexof, and substring. To the best of our knowledge, this represents one of the most expressive string constraint languages that is currently known to be decidable. Our decision procedure is based on a variant of cost register automata. We implement the decision procedure, giving rise to a new solver OSTRICH+. We evaluate the performance of OSTRICH+ on a wide range of existing and new benchmarks. The experimental results show that OSTRICH+ is the first string decision procedure capable of tackling finite transducers and integer constraints, whilst its overall performance is comparable with the state-of-the-art string constraint solvers