Jetting-based bioprinting:process,dispense physics,and applications

Jetting-based bioprinting facilitates contactless drop-on-demand deposition of subnanoliter droplets at well-defined positions to control the spatial arrangement of cells,growth factors,drugs,and biomaterials in a highly automated layer-by-layer fabrication approach.Due to its immense versatility,jetting-based bioprinting has been used for various applications,including tissue engineering and regenerative medicine,wound healing,and drug development.A lack of in-depth understanding exists in the processes that occur during jetting-based bioprinting.This review paper will comprehensively discuss the physical considerations for bioinks and printing conditions used in jetting-based bioprinting.We first present an overview of different jetting-based bioprinting techniques such as inkjet bioprinting,laser-induced forward transfer bioprinting,electrohydrodynamic jet bioprinting,acoustic bioprinting and microvalve bioprinting.Next,we provide an in-depth discussion of various considerations for bioink formulation relating to cell deposition,print chamber design,droplet formation and droplet impact.Finally,we highlight recent accomplishments in jetting-based bioprinting.We present the advantages and challenges of each method,discuss considerations relating to cell viability and protein stability,and conclude by providing insights into future directions of jetting-based bioprinting.

Exploring device physics of perovskite solar cell via machine learning with limited samples

Perovskite solar cells(PsCs)have developed tremendously over the past decade.However,the key factors influencing the power conversion efficiency(PCE)of PSCs remain incompletely understood,due to the complexity and coupling of these structural and compositional parameters.In this research,we demon-strate an effective approach to optimize PSCs performance via machine learning(ML).To address chal-lenges posed by limited samples,we propose a feature mask(FM)method,which augments training samples through feature transformation rather than synthetic data.Using this approach,squeeze-and-excitation residual network(SEResNet)model achieves an accuracy with a root-mean-square-error(RMSE)of 0.833%and a Pearson's correlation coefficient(r)of 0.980.Furthermore,we employ the permu-tation importance(PI)algorithm to investigate key features for PCE.Subsequently,we predict PCE through high-throughput screenings,in which we study the relationship between PCE and chemical com-positions.After that,we conduct experiments to validate the consistency between predicted results by ML and experimental results.In this work,ML demonstrates the capability to predict device performance,extract key parameters from complex systems,and accelerate the transition from laboratory findings to commercialapplications.

A novel encoding mechanism for particle physics

This study proposes a novel particle encoding mechanism that seamlessly incorporates the quantum properties of particles,with a specific emphasis on constituent quarks.The primary objective of this mechanism is to facilitate the digital registration and identification of a wide range of particle information.Its design ensures easy integration with different event generators and digital simulations commonly used in high-energy experiments.Moreover,this innovative framework can be easily expanded to encode complex multi-quark states comprising up to nine valence quarks and accommodating an angular momentum of up to 99/2.This versatility and scalability make it a valuable tool.

Ultrafast photoemission electron microscopy:A multidimensional probe of nonequilibrium physics

Exploring the realms of physics that extend beyond thermal equilibrium has emerged as a crucial branch of condensed matter physics research.It aims to unravel the intricate processes involving the excitations,interactions,and annihilations of quasi-and many-body particles,and ultimately to achieve the manipulation and engineering of exotic non-equilibrium quantum phases on the ultrasmall and ultrafast spatiotemporal scales.Given the inherent complexities arising from many-body dynamics,it therefore seeks a technique that has efficient and diverse detection degrees of freedom to study the underlying physics.By combining high-power femtosecond lasers with real-or momentum-space photoemission electron microscopy(PEEM),imaging excited state phenomena from multiple perspectives,including time,real space,energy,momentum,and spin,can be conveniently achieved,making it a unique technique in studying physics out of equilibrium.In this context,we overview the working principle and technical advances of the PEEM apparatus and the related laser systems,and survey key excited-state phenomena probed through this surface-sensitive methodology,including the ultrafast dynamics of electrons,excitons,plasmons,spins,etc.,in materials ranging from bulk and nano-structured metals and semiconductors to low-dimensional quantum materials.Through this review,one can further envision that time-resolved PEEM will open new avenues for investigating a variety of classical and quantum phenomena in a multidimensional parameter space,offering unprecedented and comprehensive insights into important questions in the field of condensed matter physics.

Tensile Shock Physics in Compressible Thermoviscoelastic Solid Medium

This paper addresses tensile shock physics in thermoviscoelastic (TVE) solids without memory. The mathematical model is derived using conservation and balance laws (CBL) of classical continuum mechanics (CCM), incorporating the contravariant second Piola-Kirchhoff stress tensor, the covariant Green’s strain tensor, and its rates up to order n. This mathematical model permits the study of finite deformation and finite strain compressible deformation physics with an ordered rate dissipation mechanism. Constitutive theories are derived using conjugate pairs in entropy inequality and the representation theorem. The resulting mathematical model is both thermodynamically and mathematically consistent and has closure. The solution of the initial value problems (IVPs) describing evolutions is obtained using a variationally consistent space-time coupled finite element method, derived using space-time residual functional in which the local approximations are in hpk higher-order scalar product spaces. This permits accurate description problem physics over the discretization and also permits precise a posteriori computation of the space-time residual functional, an accurate measure of the accuracy of the computed solution. Model problem studies are presented to demonstrate tensile shock formation, propagation, reflection, and interaction. A unique feature of this research is that tensile shocks can only exist in solid matter, as their existence requires a medium to be elastic (presence of strain), which is only possible in a solid medium. In tensile shock physics, a decrease in the density of the medium caused by tensile waves leads to shock formation ahead of the wave. In contrast, in compressive shocks, an increase in density and the corresponding compressive waves result in the formation of compression shocks behind of the wave. Although these are two similar phenomena, they are inherently different in nature. To our knowledge, this work has not been reported in the published literature.

基于Interactive Physics的物理习题教学--以2023年高考全国乙卷25题和湖北卷15题为例

在传统高中物理教学中,多过程碰撞的物理过程很难在板书上展现出来,成为教学的一大难点。基于Interactive Physics仿真功能,以2023年高考全国乙卷25题和湖北卷15题为例,将多过程碰撞的物理过程可视化,直观展现物理模型,帮助学生理解题目的复杂情境,有效缩短学习时间,提高教学质量和教学效率,实现最优化的教学目标。

Design and Research of an Intelligent Learning System for University Physics

In order to break through the limitations of traditional teaching,realize the integration of online and offline teaching,and optimize the intelligent learning experience of university physics,this paper proposes the design of an intelligent learning system for university physics based on cloud computing platforms,and applies this system to teaching environment of university physics.It successfully integrates emerging technologies such as cloud computing,machine learning,and situational awareness,integrates learning context awareness,intelligent recording and broadcasting,resource sharing,learning performance prediction,and content planning and recommendation,and comprehensively improves the quality of university physics teaching.It can optimize the teaching process and deepen intelligent teaching reform,aiming at providing references for the teaching practice of university physics.

Reform and Practice of University Physics Experimental Teaching Based on OBE Concept

Aiming at the problems of unclear teaching objectives,obsolete content,and single method in the experimental teaching of university physics at our university,we have implemented a series of reform initiatives.It mainly includes clarifying the student-centered teaching objectives,optimizing the experimental content,innovating the teaching methods,improving the assessment and evaluation system,and improving the experimental conditions[1,2].After the implementation of the reform,the learning effectiveness of students has been significantly improved,the teaching level of teachers has been significantly enhanced,the curriculum system has been optimized,the efficiency of teaching management has been enhanced,and social recognition has been strengthened.Practice shows that the teaching reform based on the outcome-based education concept effectively improves the quality of university physics experimental teaching and lays the foundation for cultivating innovative talents.

Integration of Ideological and Political Education in College Physics Courses Under the Reform of Smart Teaching

The development of the times has prompted China to enhance the quality of education and the value of talent.As guides for students,teachers should conscientiously implement ideological and political education,create college physics courses that are more in line with modern talent cultivation,eliminate the fixed and singular nature of traditional teaching,and find the integration points of ideological and political education.Teachers need to use the textbook itself,the expansion of resources in smart classrooms,and current technological progress to implement ideological and political education in order to cultivate more high-quality and high-level comprehensive talents for society.

Physics-based seismic analysis of ancient wood structure:fault-to-structure simulation

Based on the domain reduction method,this study employs an SEM-FEM hybrid workflow which integrates the advantages of the spectral element method(SEM)for flexible and highly efficient simulation of seismic wave propagation in a three-dimensional(3D)regional-scale geophysics model and the finite element method(FEM)for fine simulation of structural response including soil-structure interaction,and performs a physics-based simulation from initial fault rupture on an ancient wood structure.After verification of the hybrid workflow,a large-scale model of an ancient wood structure in the Beijing area,The Tower of Buddhist Incense,is established and its responses under the 1665 Tongxian earthquake and the 1730 Yiheyuan earthquake are simulated.The results from the simulated ground motion and seismic response of the wood structure under the two earthquakes demonstrate that this hybrid workflow can be employed to efficiently provide insight into the relationships between geophysical parameters and the structural response,and is of great significance toward accurate input for seismic simulation of structures under specific site and fault conditions.

Quantitative characterization of tight gas sandstone reservoirs using seismic data via an integrated rock-physics-based framework

Seismic characterizing of tight gas sandstone (TGS) reservoirs is essential for identifying promising gas-bearing regions. However, exploring the petrophysical significance of seismic-inverted elastic properties is challenging due to the complex microstructures in TGSs. Meanwhile, interbedded structures of sandstone and mudstone intensify the difficulty in accurately extracting the crucial tight sandstone properties. An integrated rock-physics-based framework is proposed to estimate the reservoir quality of TGSs from seismic data. TGSs with complex pore structures are modeled using the double-porosity model, providing a practical tool to compute rock physics templates for reservoir parameter estimation. The VP/VS ratio is utilized to predict the cumulative thickness of the TGS reservoirs within the target range via the threshold value evaluated from wireline logs for lithology discrimination. This approach also facilitates better capturing the elastic properties of the TGSs for quantitative seismic interpretation. Total porosity is estimated from P-wave impedance using the correlation obtained based on wireline log analysis. After that, the three-dimensional rock-physics templates integrated with the estimated total porosity are constructed to interpret microfracture porosity and gas saturation from velocity ratio and bulk modulus. The integrated framework can optimally estimate the parameters dominating the reservoir quality. The results of the indicator proposed based on the obtained parameters are in good agreement with the gas productions and can be utilized to predict promising TGS reservoirs. Moreover, the results suggest that considering microfracture porosity allows a more accurate prediction of high-quality reservoirs, further validating the applicability of the proposed method in the studied region.

The Historical Logic on the Basic Theory of Physics—A Summary on the Cosmic Continuum Theory

Any scientific system has a unified basic theory. But physics has no unified basic theory in the modern sense. Classical mechanics, relativity and quantum mechanics have their own basic concepts, categories and principles, so none of them can be regarded as true basic theories of physics. Cosmic Continuum Theory holds that the continuity and discreteness of the universe are fundamental issues related to the unification of physics. Because the contradiction between quantum non-locality and local reality is the fundamental obstacle to the unification of physics, while locality and non-locality correspond to the continuity and discreteness of physical reality respectively. The cosmic continuum theory introduces mathematical continuum and axiomatic ideas to reconstruct the basic theory of physics, and by the correspondence of existence and its dimensions to achieve the unification of the essence of physical reality, by introducing the cosmic continuum hypothesis to achieve the unification of the continuity and discreteness of physical reality, by introducing axiomatic methods to achieve formal unification of the foundations on physics. From the perspective of Cosmic Continuum, classical mechanics, relativity and quantum mechanics are no longer the basic theories of physics, but three branch theories of physics that are respectively applicable to macroscopic, cosmoscopic and microcosmic systems.

基于Vernier Video Physics软件的物理实验探究——以“探究平抛运动的特点”为例

智能手机的飞速发展和智能手机APP的不断更新对当今社会的各个方面都产生了深远的影响。与此同时,智能手机APP也逐渐被引入课堂教学中,为学生提供了自主学习的机会,也使物理实验教学模式发生了巨大的改变,成了广大教育者关注的热点。利用Vernier Video Physics软件和Vernier Graphical Analysis软件,再结合平抛运动实验仪,对小钢球的运动轨迹进行逐帧追踪定位,得到小钢球做平抛运动的y-x图像、x-t图像、y-t图像和v_(y)-t图像,再对图像进行相应函数拟合,经过数据分析,得到小钢球做平抛运动在水平方向和竖直方向的运动特点。利用智能手机APP辅助高中物理实验教学可以激发学生的学习兴趣,帮助学生理解物理概念和规律,使学生积极主动地参与到实验中来,培养他们的动手能力,提升他们的实验素养。

The Longmen Cloud Physics Field Experiment Base, China Meteorological Administration

Aiming at the needs of mechanism analysis of rainstorms and development of numerical prediction models in south China, the Guangzhou Institute of Tropical and Marine Meteorology of China Meteorological Administration and the Chinese Academy of Meteorological Sciences jointly set up the Longmen Cloud Physics Field Experiment Base,China Meteorological Administration. This paper introduces the instruments and field experiments of this base, provides an overview of the recent advances in retrieval algorithms of microphysical parameters, improved understanding of microphysical characteristics, as well as the formation mechanisms and numerical prediction of heavy rainfalls in south China based on the field experiments dataset.

Rock physics and seismic reflectivity parameterization and amplitude variation with offsets inversion in terms of total organic carbon indicator

Total organic carbon (TOC) prediction with elastic parameter inversions has been widely used in the identification and evaluation of source rocks. However, the elastic parameters used to predict TOC are not only determined by TOC but also depend on the other physical properties of source rocks. Besides, the TOC prediction with the elastic parameters inversion is an indirect method based on the statistical relationship obtained from well logs and experiment data. Therefore, we propose a rock physics model and define a TOC indicator mainly affected by TOC to predict TOC directly. The proposed rock physics model makes the equivalent elastic moduli of source rocks parameterized by the TOC indicator. Combining the equivalent elastic moduli of source rocks and Gray’s approximation leads to a novel linearized approximation of the P-wave reflection coefficient incorporating the TOC indicator. Model examples illustrate that the novel reflectivity approximation well agrees with the exact Zoeppritz equation until incident angles reach 40°. Convoluting the novel P-wave reflection approximation with seismic wavelets as the forward solver, an AVO inversion method based on the Bayesian theory is proposed to invert the TOC indicator with seismic data. The synthetic examples and field tests validate the feasibility and stability of the proposed AVO inversion approach. Using the inversion results of the TOC indicator, TOC is directly and accurately estimated in the target area.

Petrophysical parameters inversion for heavy oil reservoir based on a laboratory-calibrated frequency-variant rock-physics model

Heavy oil has high density and viscosity, and exhibits viscoelasticity. Gassmann's theory is not suitable for materials saturated with viscoelastic fluids. Directly applying such model leads to unreliable results for seismic inversion of heavy oil reservoir. To describe the viscoelastic behavior of heavy oil, we modeled the elastic properties of heavy oil with varying viscosity and frequency using the Cole-Cole-Maxwell (CCM) model. Then, we used a CCoherent Potential Approximation (CPA) instead of the Gassmann equations to account for the fluid effect, by extending the single-phase fluid condition to two-phase fluid (heavy oil and water) condition, so that partial saturation of heavy oil can be considered. This rock physics model establishes the relationship between the elastic modulus of reservoir rock and viscosity, frequency and saturation. The viscosity of the heavy oil and the elastic moduli and porosity of typical reservoir rock samples were measured in laboratory, which were used for calibration of the rock physics model. The well-calibrated frequency-variant CPA model was applied to the prediction of the P- and S-wave velocities in the seismic frequency range (1–100 Hz) and the inversion of petrophysical parameters for a heavy oil reservoir. The pre-stack inversion results of elastic parameters are improved compared with those results using the CPA model in the sonic logging frequency (∼10 kHz), or conventional rock physics model such as the Xu-Payne model. In addition, the inversion of the porosity of the reservoir was conducted with the simulated annealing method, and the result fits reasonably well with the logging curve and depicts the location of the heavy oil reservoir on the time slice. The application of the laboratory-calibrated CPA model provides better results with the velocity dispersion correction, suggesting the important role of accurate frequency dependent rock physics models in the seismic prediction of heavy oil reservoirs.

New physics of supersonic ruptures

Until recently,it is believed that the rupture speed above the pressure wave is impossible since spontaneously propagating ruptures are driven by the energy released due to the rupture motion,which is transferred through the medium to the rupture tip region at the maximum speed equal to the pressure wave speed.However,the apparent violation of classic theories has been revealed by new experimental results demonstrating supersonic shear ruptures.This paper presents a detailed analysis of the recently discovered shear rupture mechanism(fan hinged),which suggests a new physics of energy supply to the tip of supersonic ruptures.The key element of this mechanism is the fan‐shaped structure of the head of extreme ruptures,which is formed as a result of an intense tensile cracking process with the creation of intercrack slabs that act as hinges between the shearing rupture faces.The fan structure is featured with the following extraordinary properties:extremely low friction approaching zero;amplification of shear stresses above the material strength at low applied shear stresses;creation of a self‐disbalancing stress state causing a spontaneous rupture growth;abnormally high energy release;generation of driving energy directly at the rupture tip which excludes the need to transfer energy through the medium.The fan mechanism operates in intact rocks at stress conditions corresponding to seismogenic depths and in pre‐existing extremely smooth interfaces due to identical tensile cracking processes at these conditions.This is Paper 1(of two companion papers)which discusses the fan theory and extreme ruptures in experiments on extremely smooth interfaces.Paper 2 entitled“Fan‐hinged shear instead of frictional stick‐slip as the main and most dangerous mechanism of natural,induced and volcanic earthquakes in the earth's crust”considers extreme ruptures in intact rocks.Further study of this subject is a major challenge for deep underground science,earthquake and fracture mechanics,physics,and tribology.

Physics design of 14 MeV neutron generator facility at the Institute for Plasma Research

A high energy and high yield neutron source is a prime requirement for technological studies related to fusion reactor development. It provides a high-energy neutron environment for small-scale fusion reactor components research and testing such as tritium breeding, shielding, plasmafacing materials, reaction cross-section data study for fusion materials, etc. Along with ITER participation, the Institute of Plasma Research, India is developing an accelerator-based 14 MeV neutron source with a yield of 10^(12)n s^(-1). The design of the source is based on the deuterium–tritium fusion reaction. The deuterium beam is accelerated and delivered to the tritium target to generate 14 MeV neutrons. The deuterium beam energy and tritium availability in the tritium target are the base parameters of the accelerator-based neutron source design. The paper gives the physics design of the neutron generator facility of the Institute for Plasma Research. It covers the requirements, design basis, and physics parameters of the neutron generator. As per the analytical results generator can produce more than 1 × 10^(12)n s^(-1)with a 110 keV D^(+) ion beam of 10 mA and a minimum 5 Ci tritium target. However, the detailed simulation with the more realistic conditions of deuteron ion interaction with the tritium titanium target shows that the desired results cannot be achieved with 110 keV. The safe limit of the ion energy should be 300 keV as per the simulation. At 300 keV ion energy and 20 mA current, it reaches 1.6 × 10^(12)n s^(-1). Moreover, it was found that to ensure sufficiently long operation time a tritium target of more than 20 Ci should be used. The scope of the neutron source is not limited to the fusion reactor research studies, it is extended to other areas such as medical radioisotopes research, semiconductor devices irradiations, and many more.

Physics-constrained neural network for solving discontinuous interface K-eigenvalue problem with application to reactor physics

Machine learning-based modeling of reactor physics problems has attracted increasing interest in recent years.Despite some progress in one-dimensional problems,there is still a paucity of benchmark studies that are easy to solve using traditional numerical methods albeit still challenging using neural networks for a wide range of practical problems.We present two networks,namely the Generalized Inverse Power Method Neural Network(GIPMNN)and Physics-Constrained GIPMNN(PC-GIPIMNN)to solve K-eigenvalue problems in neutron diffusion theory.GIPMNN follows the main idea of the inverse power method and determines the lowest eigenvalue using an iterative method.The PC-GIPMNN additionally enforces conservative interface conditions for the neutron flux.Meanwhile,Deep Ritz Method(DRM)directly solves the smallest eigenvalue by minimizing the eigenvalue in Rayleigh quotient form.A comprehensive study was conducted using GIPMNN,PC-GIPMNN,and DRM to solve problems of complex spatial geometry with variant material domains from the fleld of nuclear reactor physics.The methods were compared with the standard flnite element method.The applicability and accuracy of the methods are reported and indicate that PC-GIPMNN outperforms GIPMNN and DRM.

Recent progress on fabrication and flat-band physics in 2D transition metal dichalcogenides moiré superlattices

Moiré superlattices are formed when overlaying two materials with a slight mismatch in twist angle or lattice constant. They provide a novel platform for the study of strong electronic correlations and non-trivial band topology, where emergent phenomena such as correlated insulating states, unconventional superconductivity, and quantum anomalous Hall effect are discovered. In this review, we focus on the semiconducting transition metal dichalcogenides(TMDs) based moiré systems that host intriguing flat-band physics. We first review the exfoliation methods of two-dimensional materials and the fabrication technique of their moiré structures. Secondly, we overview the progress of the optically excited moiré excitons, which render the main discovery in the early experiments on TMD moiré systems. We then introduce the formation mechanism of flat bands and their potential in the quantum simulation of the Hubbard model with tunable doping, degeneracies, and correlation strength. Finally, we briefly discuss the challenges and future perspectives of this field.