Molecular Diffusion Model Research Articles

Mitochondrial protein heterogeneity stems from the stochastic nature of co-translational protein targeting in cell senescence

A decline in mitochondrial function is a hallmark of aging and neurodegenerative diseases. It has been proposed that changes in mitochondrial morphology, including fragmentation of the tubular mitochondrial network, can lead to mitochondrial dysfunction, yet the mechanism of this loss of function is unclear. Most proteins contained within mitochondria are nuclear-encoded and must be properly targeted to the mitochondria. Here, we report that sustained mRNA localization and co-translational protein delivery leads to a heterogeneous protein distribution across fragmented mitochondria. We find that age-induced mitochondrial fragmentation drives a substantial increase in protein expression noise across fragments. Using a translational kinetic and molecular diffusion model, we find that protein expression noise is explained by the nature of stochastic compartmentalization and that co-translational protein delivery is the main contributor to increased heterogeneity. We observed that cells primarily reduce the variability in protein distribution by utilizing mitochondrial fission-fusion processes rather than relying on the mitophagy pathway. Furthermore, we are able to reduce the heterogeneity of the protein distribution by inhibiting co-translational protein targeting. This research lays the framework for a better understanding of the detrimental impact of mitochondrial fragmentation on the physiology of cells in aging and disease.

Majorization requires infinitely many second laws

Majorization is a fundamental model of uncertainty with several applications in areas ranging from thermodynamics to entanglement theory, and constitutes one of the pillars of the resource-theoretic approach to physics. Here, we improve on its relation to measurement apparatuses. In particular, after discussing what the proper notion of second law in this scenario is, we show that, for a sufficiently large state space, any family of entropy-like functions constituting a second law must be countably infinite. Moreover, we provide an analogous result for a variation of majorization known as thermo-majorization which, in fact, does not require any constraint on the state space provided the equilibrium distribution is not uniform. Finally, we discuss the applicability of our results to molecular diffusion and catalytic majorization. In this regard, we consider a variation of majorization used in plasma physics as a model of molecular diffusion and show that no finite family of entropy-like functions constituting a second law of molecular diffusion exists. Moreover, we show how our results are useful when dealing with a conjecture regarding catalytic majorization (i.e. trumping). In particular, we show that the sort of characterizations of trumping that have been considered before require an infinite family of real-valued functions.

Geometry-complete diffusion for 3D molecule generation and optimization

Generative deep learning methods have recently been proposed for generating 3D molecules using equivariant graph neural networks (GNNs) within a denoising diffusion framework. However, such methods are unable to learn important geometric properties of 3D molecules, as they adopt molecule-agnostic and non-geometric GNNs as their 3D graph denoising networks, which notably hinders their ability to generate valid large 3D molecules. In this work, we address these gaps by introducing the Geometry-Complete Diffusion Model (GCDM) for 3D molecule generation, which outperforms existing 3D molecular diffusion models by significant margins across conditional and unconditional settings for the QM9 dataset and the larger GEOM-Drugs dataset, respectively. Importantly, we demonstrate that GCDM’s generative denoising process enables the model to generate a significant proportion of valid and energetically-stable large molecules at the scale of GEOM-Drugs, whereas previous methods fail to do so with the features they learn. Additionally, we show that extensions of GCDM can not only effectively design 3D molecules for specific protein pockets but can be repurposed to consistently optimize the geometry and chemical composition of existing 3D molecules for molecular stability and property specificity, demonstrating new versatility of molecular diffusion models. Code and data are freely available on GitHub.

Numerical investigation of hydroxyl radicals produced by a single bubble in jet pump cavitation reactor

Jet pump cavitation reactors (JPCRs) have significant potential to be used in water treatment applications. During their operation, the hydroxyl radicals generated by cavitation collapse produce a strong oxidation capacity, which is one of the key mechanisms in disrupting algal cells. In this paper, we investigate the hydroxyl radicals produced by single cavitation bubble in a JPCR. The numerical method includes a bubble dynamic model, molecular diffusion model, energy balance equation, and chemical reaction model for predicting the hydroxyl radical production. Additionally, the pressure distribution within a JPCR is tested and used to analyze the single-bubble performance. The effects of the JPCR operating conditions and structure parameters on hydroxyl radical production are further discussed. Our results indicate that, when the flow rate ratio is positive, the number of hydroxyl radicals is closely related to the development and collapse of the cavitation bubble and reaches a peak value under the critical condition. When the flow rate ratio is negative, the maximum production of hydroxyl radicals appears under backflow stagnation condition. In general, increasing the throat length–diameter ratio and diffuser angle encourages the production of hydroxyl radicals, whereas increasing the area ratio inhibits their generation.

Comment: AI Accelerates Modeling of New Materials for Carbon Capture

_ Artificial intelligence (AI) is increasingly being employed to assist in the development of materials, including metal-organic frameworks (MOFs), to develop carbon capture technologies. MOFs are modular materials made up of three building blocks: inorganic nodes such as zinc or copper; organic nodes; and organic linkers made up of carbon, oxygen, and other elements. By changing the relative positions and configurations of the building blocks, the potential combinations for creation of unique MOFs are countless. The idea is to create a porous carbon dioxide “trap” to capture carbon from the air. The structure created by the building blocks can be thought of simplistically as a scaffolding with joints (linkers) that functions to absorb carbon. In a recent paper, researchers from the US Department of Energy Argonne National Laboratory, The University of Illinois Chicago (UIC), University of Illinois, Northwestern University, and TotalEnergies described their use of generative AI to identify “good carbon absorbers” among “billions and billions of possibilities.” Although early work on developing MOFs began in the 1990s, the power of AI modeling can generate new models with desired properties such as optimal selectivity and capacity without the laborious, reiterative experimental and computational efforts once required. In a press release, Argonne said, “… The team was able to quickly assemble, building block by building block, over 120,000 new MOF candidates within 30 minutes” on a supercomputer at the Argonne Leadership Computing Facility. “The race for capturing carbon hinges on finding needles in a haystack, and trial and error is too slow. You have billions and billions of possibilities, and then you must narrow down to candidates that are good carbon absorbers,” said paper coauthor Santanu Chaudhuri, professor of civil, materials, and environmental engineering at UIC and director of manufacturing science and engineering at Argonne, in a news release. “With this project, we have taken the first significant step towards closing that gap by using generative AI.” Coauthor Eliu Huerta, an Argonne computational scientist who helped lead the study, said, “The traditional methods have typically involved experimental synthesis and computational modeling with molecular dynamics simulations. But trying to survey the vast MOF landscape in this way is just impractical. “We wanted to add new flavors to the MOFs that we were designing. We needed new ingredients for the AI recipe,” Huerta said. The AI framework, called GHP-MOFassemble, screened the newly built 120,000 MOFs in 40 minutes to identify nearly 79,000 with valid bonds (joints). Those were screened to identify 19,000 with valid chemistry within 205 minutes. From this group, 364 MOFs were identified in 50 minutes with CO2 capacity higher than a selected value. From assembly to selection of MOFs, GHP-MOFassemble completed the analysis within 5 hours and 7 minutes. Powering the AI Future It’s ironic that processing of massive volumes of data on supercomputers for emissions-related research, such as AI simulations, could contribute to the problem to be solved because of the electricity required. Supercomputers use the computing power of multiple interconnected processing cores, which require an immense amount of energy—and depending on the source of the electricity generation, also contribute to emissions. In a November 2023 commentary, International Energy Agency (IEA) analysts estimated that AI uses more energy than other forms of computing. Training a single model uses more electricity than 100 US homes consume in an entire year. In 2022, Google reported that machine learning accounted for about 15% of its total energy use over the prior 3 years. In a January report, IEA estimated that Google could see a tenfold increase in their electricity demand if AI is fully implemented in its search engine. The average electricity demand of a typical search is 0.3 Wh. OpenAI’s ChatGPT requires 2.9 Wh per request. Considering 9 billion searches per day, nearly 10 TWh of additional electricity would be required in a year. Google and other tech companies are shifting their data operations around the world daily or hourly to tap into excess renewable energy production. Google uses its “carbon-intelligent” platform to analyze day-ahead predictions of how much a given grid will be relying on carbon-intensive energy. It then shifts computing globally in favor of regions where more carbon-free electricity is available. As AI advances in modeling complexity with a greater number of parameters, there is a growing emphasis on sustainability and optimizing power consumption, including developing energy-efficient algorithms, hardware, and data center management strategies. For Further Reading A Generative Artificial Intelligence Framework Based on a Molecular Diffusion Model for the Design of Metal-Organic Frameworks for Carbon Capture by H. Park, X. Yan, and R. Zhu, et al. Communications Chemistry. Why AI and Energy Are the New Power Couple by V. Rozite, J. Miller, and S. Oh, International Energy Agency. Electricity 2024. International Energy Agency. We Now Do More Computing Where There’s Cleaner Energy by R. Koningstein, Google Research.

A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture

Metal-organic frameworks (MOFs) exhibit great promise for CO2 capture. However, finding the best performing materials poses computational and experimental grand challenges in view of the vast chemical space of potential building blocks. Here, we introduce GHP-MOFassemble, a generative artificial intelligence (AI), high performance framework for the rational and accelerated design of MOFs with high CO2 adsorption capacity and synthesizable linkers. GHP-MOFassemble generates novel linkers, assembled with one of three pre-selected metal nodes (Cu paddlewheel, Zn paddlewheel, Zn tetramer) into MOFs in a primitive cubic topology. GHP-MOFassemble screens and validates AI-generated MOFs for uniqueness, synthesizability, structural validity, uses molecular dynamics simulations to study their stability and chemical consistency, and crystal graph neural networks and Grand Canonical Monte Carlo simulations to quantify their CO2 adsorption capacities. We present the top six AI-generated MOFs with CO2 capacities greater than 2m mol g−1, i.e., higher than 96.9% of structures in the hypothetical MOF dataset.

Numerical Investigation of Partially Premixed Flames Under Transcritical Conditions

Understanding the characteristics of partially premixed flames (PPFs) under transcritical conditions is of critical importance for the development of fuel-rich staged rocket engines. While substantial progress has been achieved for transcritical non-premixed flames (NPFs), comparatively little effort has been made to investigate transcritical PPFs. To this end, a series of transcritical counterflow gaseous hydrogen/liquid oxygen (GH2/LOX) PPFs is simulated to investigate the thermodynamic structure of PPF and to examine the effects of molecular diffusion modeling, strain rate, and the equivalence ratio at the fuel-rich side on PPFs in physical space and mixture fraction space, as well as reduced temperature/reduced pressure space. The comparisons between the NPF and PPF demonstrate that the PPF exhibits a bimodal structure in physical space: a premixed reaction zone at the fuel inlet side and a non-premixed reaction zone at the oxidizer side. In mixture fraction space, the C-shaped structure of PPF is observed owing to the differential diffusion of species. It is found that the choice of molecular diffusion model has a significant impact on PPF structure. The presence of a loop at subcritical pressures in a reduced temperature/reduced pressure space is caused by the differential diffusion of species and the formation of [Formula: see text] in the non-premixed reaction zone. Furthermore, the results indicate that the premixed reaction zones in the PPFs are very sensitive to the change in strain rate and/or equivalence ratio of the premixed mixture at the fuel inlet side. For a given equivalence ratio, increasing strain rate can suppress the differential diffusion effect and the C-shaped structure, while it has a negligible impact on the non-premixed reaction and hence on loop formation.

Mathematical and computational modeling of membrane distillation technology: A data-driven review

Membrane distillation (MD) technology is increasingly gaining attention as an environmentally sustainable water treatment method of emerging interest. During last three decades there has been wide efforts to model and improve the performance of this technology. In this study we examine both the mathematical and computational modeling methods used in MD with a data-driven method. To gather the dataset, a broad range of terms related with theoretical modeling of MD were searched in the Scopus database. The collection consists of 526 documents including 116 journals, 14,291 references used by authors, 1252 involved authors and 29.47 % international co-authorship rate. The overall pattern of publications is found to increase over time indicating the enhancing interest on theoretical modeling of MD process. Journal of Membrane Science and Desalination are the top two journals publishing theoretical modeling of MD, with 105 and 100 articles, respectively. Dr. Ghaffour N. contributed with the highest number of articles, 24; and Dr. Khayet M. has the highest articles fractionalized value with 7.08. The dataset was categorized first into mathematical and computational modeling, then into the used mass transport approaches through membrane hydrophobic pores. Recently, in MD field computational modeling has been considered more than mathematical modeling. The combined Knudsen diffusion/ordinary molecular diffusion model is the dominant mass transport approach considered in MD mathematical modeling with 117 articles. On the other hand, computational fluid dynamics is the most used computational method with 114 articles.

Carbon dioxide foam bubbles enhance skin penetration through the stratum corneum layer with mechanical mechanism

Topical skin formulations often include penetration enhancers that interact with the outer stratum corneum (SC) layer to chemically enhance diffusion. Alternatively, penetration can be mechanically enhanced with simple rubbing in the presence of solid particles sometimes included to exfoliate the top layers of the SC. Our goal was to evaluate micron-sized carbon dioxide bubbles included in a foamed moisturizing formulation as a mechanical penetration enhancement strategy. We show that moisturizing foam bubbles cause an increase in SC formulation penetration using both mechanical and spectroscopic characterization. Our results suggest viscous liquid film drainage between coalescing gaseous bubbles creates local regions of increased hydrodynamic pressure in the foam liquid layer adjacent to the SC surface that enhances treatment penetration. An SC molecular diffusion model is used to rationalize the observed behavior. The findings indicate marked increased levels of treatment concentration in the SC at 2 h and that persists to 18 h after exposure, far exceeding non-foamed treatments. The study suggests an alternate strategy for increasing formulation penetration with a non-chemical mechanism.

Direct numerical simulations of polypropylene gasification in supercritical water

In order to reduce environmental pollution by plastic wastes, supercritical water gasification (SCWG) appears as a promising technology. The present study investigates the SCWG process of polypropylene (PP) plastic waste using particle-resolved direct numerical simulations (PR-DNS). A directional ghost-cell immersed boundary method has been used to solve the reacting boundary condition, including detailed molecular diffusion models. To validate the procedure, SCWG of a coal particle has been first investigated as a benchmark, analyzing in detail interphase momentum and heat and mass transfer, and chemical reactions are analyzed. Surface reactions and the resulting Stefan flow expand the boundary layer around the particle, impacting the efficiency of heat and mass transfer. Adding then a suitable reaction mechanism, SCWG of PP plastic wastes leading to combustible gases is analyzed by PR-DNS and found to be very efficient. The gasification temperature is an important parameter to control SCWG efficiency. To the authors' knowledge, this is the first PR-DNS study investigating the SCWG process for plastic wastes, and it provides interesting information regarding transfer processes and their limitations.

Transdermal and lateral effective diffusivities for drug transport in stratum corneum from a microscopic anisotropic diffusion model

This paper presents a computational model of molecular diffusion through the interfollicular stratum corneum. Specifically, it extends an earlier two-dimensional microscopic model for the permeability in two ways: (1) a microporous leakage pathway through the intercellular lipid lamellae allows slow permeation of highly hydrophilic permeants through the tissue; and (2) the model yields explicit predictions of both lateral (D‾‖sc) and transdermal (D‾⊥sc) effective (average, homogenized) diffusivities of solutes within the tissue. We present here the mathematical framework for the analysis and a comparison of the predictions with experimental data on desorption of both hydrophilic and lipophilic solutes from human stratum corneum in vitro. Diffusion in the lipid lamellae is found to make the effective diffusivity highly anisotropic, with the predicted ratio D‾‖sc/D‾⊥sc ranging from 34 to 39 for fully hydrated skin and 150 to more than 1000 for partially hydrated skin. The diffusivities and their ratio are in accord with both experimental data and the results of mathematical analyses performed by others.

Synthesis of Spherical Composite CMC-LTO-EGDE-ME for Lithium Recovery from Geothermal Water

In this study, ethylene glycol diglycidyl ether (EGDE) and melamine (ME) were used to prepare a spherical composite material CMC-LTO-EGDE-ME through the strategy of cross-linking reaction of biodegradable sodium carboxymethyl cellulose (CMC) with Li2TiO3 (LTO) to improve the cycle stability of the adsorption-desorption for lithium recovery. In the geothermal water at 333.15°K, the adsorption capacity of the spherical composite adsorbent for lithium-ion is 12.02 mg/g, and the adsorption equilibrium time is 8 h. There is a good selectivity of Li+ (Kd = 3.7 × 103) and high separation factors between Li+ and Na+, K+, Cs+ and Ca2+ (between 39.17 and 181.97). Studies on adsorption kinetics and adsorption isotherm showed that composite material’s adsorption process was obtained from the pseudosecond-order kinetics and the molecular diffusion model. It was found that the composite material has broad applications in lithium recovery from geothermal water.

Simulation of diffusion in dense molecular systems on ARUZ – Massively-parallel FPGA-based machine

In this paper, the implementation of a molecular diffusion model named Dynamic Lattice Liquid (DLL) is discussed. Two algorithms, sequential and parallel, implementing this model are examined. The presented theoretical considerations indicate some limitations of the sequential approach. The proposed parallel algorithm overcomes these limitations, but it requires dedicated hardware. An example of such hardware, the FPGA-based HPC system named ARUZ, is presented.

Quantitating Diffusion Enhancement in Pore Hierarchies.

A microporous continuum traversed by a set of mutually perpendicular channels is considered to be a model for a hierarchically porous system of the mesoporous zeolite type. Transient profiles of molecular uptake as determined by kinetic Monte Carlo (kMC) simulation are found to be in excellent agreement with the result attained by the application of the two-region model (the Kärger model) of molecular diffusion. In particular, it is found that, in the two limiting cases referred to as fast exchange and slow exchange, there exist two simple analytical expressions for the rate of molecular uptake and hence for the quantification of transport enhancement in comparison with the purely microporous adsorbent. In the general case, transport enhancement is simply recognized by the reciprocal addition of the expressions in the two limiting cases.

Numerical investigation of wax deposition features in a pipeline under laminar flow conditions

Wax deposit inside pipelines continues to be a critical issue in the oil and gas industry. The available wax deposition data in the literature are currently insufficient to construct viable predictive numerical methods that capture all wax deposit features. Therefore, more research studies are required to improve our understanding of the physics of wax-deposition phenomena. In the present paper, a numerical study is performed to predict the temporal and spatial distribution of the porous wax deposit during laminar flow in a pipe. A mathematical model which combines the energy and momentum balance equations and molecular diffusion model by Fick's law is employed to better describe the wax deposit. Validation with experimental data as well as numerical results and characteristics of wax deposition are presented. The results revealed that an increase in the deposition time and porosity leads to a significant increase in the wax deposit content and pressure drop, and a decrease in the fluid temperature, heat transfer coefficient, and flow rate. However, an increase in porosity leads to larger variation of these parameters over a short period of time. Further, it is demonstrated that the wax deposit is concentrated over a short axial length, and its maximum which appears at X/L = 0.014 is kept unchanged with time and porosity variation.

Structure-dependent recruitment and diffusion of guest proteins in liquid droplets of FUS

Liquid droplets of a host protein, formed by liquid–liquid phase separation, recruit guest proteins and provide functional fields. Recruitment into p53 droplets is similar between disordered and folded guest proteins, whereas the diffusion of guest proteins inside droplets depends on their structural types. In this study, to elucidate how the recruitment and diffusion properties of guest proteins are affected by a host protein, we characterized the properties of guest proteins in fused in sarcoma (FUS) droplets using single-molecule fluorescence microscopy in comparison with p53 droplets. Unlike p53 droplets, disordered guest proteins were recruited into FUS droplets more efficiently than folded guest proteins, suggesting physical exclusion of the folded proteins from the small voids of the droplet. The recruitment did not appear to depend on the physical parameters (electrostatic or cation–π) of guests, implying that molecular size exclusion limits intermolecular interaction-assisted uptake. The diffusion of disordered guest proteins was comparable to that of the host FUS, whereas that of folded proteins varied widely, similar to the results for host p53. The scaling exponent of diffusion highlights the molecular sieving of large folded proteins in droplets. Finally, we proposed a molecular recruitment and diffusion model for guest proteins in FUS droplets.

The development of high temperature heat-pipe transient model for system analysis of heat pipe cooled microreactor

The miniaturization of nuclear reactor power is the direction of nuclear energy development in the future. A nuclear power system with a small solid core and cooled by a high-temperature alkali metal heat pipe has excellent safety performance. The start-up of the heat pipe cooled reactor depends on the heat pipe start-up characteristics from the frozen state, so transient analysis models of high-temperature heat pipe from the frozen state are established in this paper for the system characteristics analysis of the heat pipe cooled microreactor. The models include three stages of high-temperature heat pipe start-up from the frozen state: the molecular diffusion model of the free molecular stage, the flat-front model of the transitional flow process, and the 2-D thermal resistance network model of continuous flow. The energy conservation model is also used to consider the phase transition between solid, liquid, and gas. And The heat transfer limit model of heat pipe has been also coupled in the model. The models have been verified by the start-up experimental dates of the sodium heat pipe and the transient experimental dates of the water heat pipe. Then the models are applied to the system analysis code of the heat pipe reactor to analyze the transient experiment characteristics of a typical heat pipe reactor system. The results show that the predicted trend by HPMR system code is reasonable and the deviation with experimental data is less than 2%, which further demonstrates the applicability and effectiveness of the heat pipe transient models.

Nuclear compartmentalization as a mechanism of quantitative control of gene expression.

Gene regulation requires the dynamic coordination of hundreds of regulatory factors at precise genomic and RNA targets. Although many regulatory factors have specific affinity for their nucleic acid targets, molecular diffusion and affinity models alone cannot explain many of the quantitative features of gene regulation in the nucleus. One emerging explanation for these quantitative properties is that DNA, RNA and proteins organize within precise, 3D compartments in the nucleus to concentrate groups of functionally related molecules. Recently, nucleic acids and proteins involved in many important nuclear processes have been shown to engage in cooperative interactions, which lead to the formation of condensates that partition the nucleus. In this Review, we discuss an emerging perspective of gene regulation, which moves away from classic models of stoichiometric interactions towards an understanding of how spatial compartmentalization can lead to non-stoichiometric molecular interactions and non-linear regulatory behaviours. We describe key mechanisms of nuclear compartment formation, including emerging roles for non-coding RNAs in facilitating their formation, and discuss the functional role of nuclear compartments in transcription regulation, co-transcriptional and post-transcriptional RNA processing, and higher-order chromatin regulation. More generally, we discuss how compartmentalization may explain important quantitative aspects of gene regulation.

Analysis of the Biochemical Reaction Status by Real-Time Monitoring Molecular Diffusion Behaviors Using a Transistor Biosensor Integrated with a Microfluidic Channel.

Traditional methods of monitoring biochemical reactions measure certain detectable reagents or products while assuming that the undetectable species follow the stoichiometry of the reactions. Here, based upon the metal-oxide thin-film transistor (TFT) biosensor, we develop a real-time molecular diffusion model to benchmark the concentration of the reagents and products. Using the nicotinamide adenine dinucleotide (NADH)–oxaloacetic acid with the enzyme of malate dehydrogenase as an example, mixtures of different reagent concentrations were characterized to extract the ratio of remaining concentrations between NAD+ and NADH. We can thus obtain the apparent equilibrium constant of the reaction, (8.06 ± 0.61) × 104. Because the whole analysis was conducted using a TFT sensor fabricated using a semiconductor process, our approach has the advantages of exploring biochemical reaction kinetics in a massively parallel manner.

Label‐Free Phase Change Detection of Lipid Bilayers Using Nanoscale Diamond Magnetometry

AbstractThe nitrogen‐vacancy (NV) center in diamond is a quantum sensor with exceptional quality for highly sensitive nanoscale analysis of nuclear magnetic resonance (NMR) spectra and thermometry. In this study, nanoscale phase change detection of lipid bilayers is investigated utilizing ensemble‐averaged nuclear spin detection from small volume ≈(6 nm)3, which is determined by the depth of the NV center. Analysis of nanoscale NMR signal confirms thickness of lipid bilayer to be 6.2 nm ± 3.4 nm with proton density of 65 protons per nm3 on top of diamond sample. The result of the correlation spectroscopy from nanoscale volume is compared with the 2D molecular diffusion model constructed by Monte Carlo simulation combined with results from molecular dynamics (MD) simulation. There is a change in diffusion constant from 1.5 ± 0.25 nm2 µs−1 to 3.0 ± 0.5 nm2 µs−1 when the temperature changes from 26.5 to 36.0 °C. The results demonstrate that the multi‐parameter detection of changes in translational diffusion and temperature is possible in label‐free measurements using nanoscale diamond magnetometry. The method paves the way for label‐free imaging of cell membranes for understanding their phase composition and dynamics.