Energy Landscape Research Articles

Reprogrammable 3D Shapes from 1D Metamaterial

AbstractThree billion years of evolution have produced a vast variety of protein molecules, whose functions are directly dependent on their ability to assume and maintain specific shapes. Proteins are defined by the sequence and chemical characteristics of their amino acids, which dictate their 3D shape and function, ranging from enzymatic activity to immune responses. Here, we explore a synthetic form of linear structure that can be bent in a programmable way into various specific 3D shapes inspired by the way functional proteins are defined using genetic codes. This synthetic structure is based on non‐circular multistable corrugated tubes, which can be fabricated at various length scales and cross‐sectional shapes, thus enabling the modification of their properties. Additionally, the cross‐section shape can be rewritten multiple times, allowing for the repair of structural damage and the rewriting of the properties of the structure's multi‐stability. A numerical model is used to describe the bending energy landscape of different cross‐sections. The proposed reprogrammable 3D shapes of a rewritable 1D metamaterial are promising candidates for futuristic robotic systems, complex deployable structures, catheter devices, and energy absorption and harvesting.

Elucidating the role of PPARG inhibition in enhancing MERS virus immune response: A network pharmacology and computational drug discovery

BackgroundMiddle East Respiratory Syndrome (MERS) has become a severe zoonotic disease, posing significant public health concerns due to the lack of specific medications. This urgently demands the development of novel therapeutic molecules. Understanding MERS's genetic underpinnings and potential therapeutic targets is crucial for developing effective treatments. MethodsTwo gene expression datasets (GSE81909 and GSE100504) were analyzed to identify differentially expressed genes (DEGs) using GEO2R. Furthermore, gene ontology (GO), pathway enrichment analysis, and protein-protein interaction (PPI) network were performed to understand the gene’s functions. A possible drug target was identified, and an FDA-approved drug library was screened against the selected target using molecular docking and validated the findings through molecular dynamics simulation, principal component analysis, free energy landscape, and MM/GBSA calculations. ResultsThe study on GSE81909 and GSE100504 datasets with icMERS and MOCK samples at 24 and 48 h revealed an upregulation in 73 and 267 DEGs, respectively. In the network pharmacology, STAT1, MX1, DDX58, EIF2AK2, ISG15, IFIT1, IFIH1, OAS1, IRF9, and OASL were identified as the top 10 hub genes. STAT1 was identified as the most connected hub gene among these top 10 hub genes, which plays a crucial role in the immune response to the MERS virus. Further study on STAT1 showed that PPARG helps reduce STAT1, which could modulate the immune response. Therefore, by inhibiting PPARG, the immunological response can be successfully enhanced. The known inhibitor of PPARG, 570 (Farglitazar), was used as a control. Further, screening using Tanimoto and K-mean clustering was performed, from which three compounds were identified: 2267, 3478, and 40326. Compound 3478 showed characteristics similar to the control, indicating robust binding to PPARG. 3478 showed the highest negative binding free energy with −41.20 kcal/mol, indicating strong binding with PPARG. ConclusionsThese findings suggest that 3478 promises to be a potential inhibitor of PPARG, and further experimental investigations can explore its potential as a MERS inhibitor.

Multistable Composite Laminate Grids as a Design Tool for Soft Reconfigurable Multirotors

Adaptive‐morphology multirotors exhibit superior versatility and task‐specific performance compared to traditional multirotors owing to their functional morphological adaptability. However, a notable challenge lies in the contrasting requirements of locking each morphology for flight controllability and efficiency while permitting low‐energy reconfiguration. A novel design approach is proposed for reconfigurable multirotors utilizing soft multistable composite laminate airframes. These airframes show kinematically determinate morphologies corresponding to multiple minima in their elastic potential energy landscape. By varying design parameters, the methodology allows for tuning the energy landscape characteristics governing each morphology's structural stability and reconfiguration energetics. The airframe, composed of multistable composite laminate grids, is optimized to maximize rigidity under flight loads and minimize reconfiguration work. The 130‐g reconfigurable multirotor design demonstrates self‐locking properties in an open and a folded configuration, enabling a 48% reduction in width‐span without compromising stability during flight. Soft pneumatic actuators, actuated using a tethered compressed air supply, enable reversible reconfiguration on the ground between open and folded configurations. The design resolves the conflicting requirements of high‐stiffness to lock each flight configuration and low‐actuation work for reconfigurability. By exploiting soft yet multistable structures, the approach combines the stability observed in rigid‐linked reconfigurable multirotors with the low‐effort reconfigurability of soft multirotors, offering new methods for designing adaptive‐morphology multirotors.

Illuminating tandem reactions characterized by temporal separation of catalytic activities via DFT calculations: A case study of Ni-catalyzed alkyne semi-hydrogenation

The concept of “temporal separation of catalytic activities” outlines a scenario where multiple transformations within a catalytic tandem reaction proceed sequentially over time without mutual interference. After presenting several examples of such reactions, we specifically focus on an example of the Ni-catalyzed alkyne semi-hydrogenation as a significant case study. By performing density functional theory (DFT) calculations, we illuminate the unique dynamic character of the reaction that the intermediate remains dormant until the reactant exhausted. The insights gained from the present calculations have led us to propose a comprehensive energy landscape model for the catalytic tandem reactions with temporal separation of catalytic activities, which offers a logical explanation for the temporal dormancy of the intermediate. This class of reactions is expected to be highly valuable as it presents the opportunity to fine-tune individual reaction steps, thereby introducing fresh concepts for precise control of reactions in one-pot chemistry.

Phase Distribution Dictates Charge Transfer and Transport Dynamics in Layered Quasi-2D Perovskite.

Strategic manipulation of spatiotemporal evolution of charge carriers is critical for optimizing performance of quasi-two-dimensional (2D) perovskite-based optoelectronic devices. Nonetheless, the inhomogeneous phase distribution and band alignment engender intricate energy landscapes, complicating internal charge and energy funneling processes. Herein, we integrate high spatiotemporal resolution transient absorption microscopy with multiple time-resolved spectroscopy and find that asynchronous electron and hole transfers rather than direct energy transfer govern the funneling mechanisms. Notably, the charge funneling pathways and transport behaviors can be modifiable by phase manipulation. The accumulation of small-n phases suppresses the electron funneling toward large-n phases and doubles the carrier diffusion rate from 0.085 to 0.20 cm2/s, yielding a 1.5-fold enhancement in diffusion length. Phase order engineering is further corroborated for facilitating charge separation. Our investigation underscores the prospects of manipulating the phase distribution to control internal charge funneling and transport, thereby substantiating the theoretical foundations for optimizing optoelectronic devices.

A computational and machine learning approach to identify GPR40-targeting agonists for neurodegenerative disease treatment.

The G protein-coupled receptor 40 (GPR40) is known to exert a significant influence on neurogenesis and neurodevelopment within the central nervous system of both humans and rodents. Research findings indicate that the activation of GPR40 by an agonist has been observed to promote the proliferation and viability of hypothalamus cells in the human body. The objective of the present study is to discover new agonist compounds for the GPR40 protein through the utilization of machine learning and pharmacophore-based screening techniques, in conjunction with other computational methodologies such as docking, molecular dynamics simulations, free energy calculations, and investigations of the free energy landscape. In the course of our investigation, we successfully identified five unreported agonist compounds that exhibit robust docking score, displayed stability in ligand RMSD and consistent hydrogen bonding with the receptor in the MD trajectories. Free energy calculations were observed to be higher than control molecule. The measured binding affinities of compounds namely 1, 3, 4, 6 and 10 were -13.9, -13.5, -13.4, -12.9, and -12.1 Kcal/mol, respectively. The identified molecular agonist that has been found can be assessed in terms of its therapeutic efficacy in the treatment of neurological diseases.

Construction of Continuous Collective Energy Landscapes for Large Amplitude Nuclear Many-Body Problems.

Several protocols are proposed to build continuous energy surfaces of many-body quantum systems, regarding both energy and states. The standard variational principle is augmented with constraints on state overlap, ensuring arbitrary precision on continuity. As an illustration, the lowest energy and excited state paths relevant for the ^{240}Pu asymmetric fission are studied. The scission is clearly signed, with a neutron excess in the neck, the ultimate glue before its rupture. Our approach can potentially connect any couple of Hilbert space states, which opens up new horizons for various applications.

A Deep Learning-Driven Sampling Technique to Explore the Phase Space of an RNA Stem-Loop.

The folding and unfolding of RNA stem-loops are critical biological processes; however, their computational studies are often hampered by the ruggedness of their folding landscape, necessitating long simulation times at the atomistic scale. Here, we adapted DeepDriveMD (DDMD), an advanced deep learning-driven sampling technique originally developed for protein folding, to address the challenges of RNA stem-loop folding. Although tempering- and order parameter-based techniques are commonly used for similar rare-event problems, the computational costs or the need for a priori knowledge about the system often present a challenge in their effective use. DDMD overcomes these challenges by adaptively learning from an ensemble of running MD simulations using generic contact maps as the raw input. DeepDriveMD enables on-the-fly learning of a low-dimensional latent representation and guides the simulation toward the undersampled regions while optimizing the resources to explore the relevant parts of the phase space. We showed that DDMD estimates the free energy landscape of the RNA stem-loop reasonably well at room temperature. Our simulation framework runs at a constant temperature without external biasing potential, hence preserving the information on transition rates, with a computational cost much lower than that of the simulations performed with external biasing potentials. We also introduced a reweighting strategy for obtaining unbiased free energy surfaces and presented a qualitative analysis of the latent space. This analysis showed that the latent space captures the relevant slow degrees of freedom for the RNA folding problem of interest. Finally, throughout the manuscript, we outlined how different parameters are selected and optimized to adapt DDMD for this system. We believe this compendium of decision-making processes will help new users adapt this technique for the rare-event sampling problems of their interest.

Machine learning-augmented modeling on the formation of Si-dominated Non-β″ early-stage precipitates in Al-Si-Mg alloys with Si supersaturation induced by non-equilibrium solidification

Recent experiments have shown that Al-Si-Mg alloys solidified under high cooling rates may lead to the nucleation of Si-enriched clusters that are remarkably different from the conventional Mg-Si co-clusters (e.g. β″ particles), and yet the responsible mechanism remains to be elucidated. Here we tackle the problem using a multiscale modeling framework that integrates atomistic modeling, energy landscape sampling, and lattice-based kinetic Monte Carlo (kMC) simulation. The migration energy barriers for vacancy-mediated diffusion amid complex local chemical environments are predicted on-the-fly using a surrogate machine learning model. We discover that the actual vacancy-Si migration barriers are much lower than those assumed in the classic linear interpolation approximation. Such a strong deviation from conventional wisdom, in conjunction with differing Si solute composition, can lead to a great variety in the nucleated early-stage precipitates. More specifically, a high-level supersaturation of Si solute (i.e.xSi/(xSi+xMg)>0.75) would lead to an unexpectedly high enrichment of Si in the nucleated clusters with the Si:Mg ratio up to 5∼6; while at a lower-level supply of Si solute the Mg-Si co-clusters (i.e. Si:Mg ratio around 1∼2) are nucleated instead. These findings provide a viable explanation for the diverse types of early-stage precipitates observed in various experiments, from Si-enriched precipitates in high-pressure die cast Al alloys to β″ particles in conventional casting and/or heat-treated alloys. The implications of our findings are also discussed.

Energy transition in the oil and gas sector: Business models for a sustainable future

The global energy landscape is undergoing a significant transformation as industries shift towards more sustainable practices, driven by environmental regulations, societal pressure, and technological advancements. The oil and gas sector, traditionally dependent on fossil fuels, is at the forefront of this energy transition. This review explores the evolving business models that facilitate the industry's transition to a low-carbon economy, focusing on strategies for achieving sustainability while maintaining profitability. Key approaches include diversifying energy portfolios by investing in renewable energy sources such as wind, solar, and hydrogen, as well as enhancing operational efficiency through digital technologies like artificial intelligence and the Internet of Things (IoT). The transition is also marked by an increased focus on carbon capture, utilization, and storage (CCUS) technologies, which mitigate the environmental impact of traditional oil and gas operations. Business models are being reshaped to incorporate circular economy principles, where waste and emissions are minimized through innovation and resource optimization. Furthermore, partnerships between oil and gas companies and renewable energy firms are emerging as a critical pathway for mutual growth and sustainability. This energy transition also involves rethinking the workforce and reskilling employees for emerging sectors, emphasizing the need for adaptability in business models. Regulatory frameworks play a pivotal role in shaping these models, as policies increasingly favor green investments and low-carbon initiatives. As oil and gas companies diversify their portfolios, they are creating new revenue streams that align with global decarbonization goals while ensuring financial resilience. This study highlights how these innovative business models are setting the foundation for a sustainable future in the oil and gas sector, ensuring that the industry remains relevant in a rapidly changing energy ecosystem. Keywords: Energy Transition, Oil and Gas, Business Models, Sustainability, Renewable Energy, Carbon Capture, Circular Economy, Digital Transformation, Decarbonization, Low-Carbon Economy.

Energy landscape and flow dynamics measurements of driven-dissipative systems

Many experimental techniques aim at determining the energy landscape of a system given and compare it to a model Hamiltonian. This landscape governs the system's evolution in the absence of dissipation. Here, we theoretically propose and experimentally demonstrate a method to measure the energy landscape of a system without knowing its functional form. A crucial ingredient for our method is the presence of dissipation, which enables sampling of the landscape over a large area of phase space through ringdown-type measurements, overcoming the main limitation of previous techniques. We apply the method to a driven-dissipative system–a parametric oscillator–observed in a rotating frame. We first measure the phase-space flow dynamics of the system via ringdown measurements, unveiling its attractors and separatrices. With these measurements, we reconstruct the (quasi-)energy landscape of the system. Furthermore, we demonstrate that our method provides direct experimental access to the so-called symplectic norm of the stationary states of the system, which is tied to the particle- or holelike nature of excitations of these states. In this way, we establish a method to identify qualitative differences between the fluctuations around stabilized minima and maxima of the nonlinear out-of-equilibrium stationary states. Our method constitutes a versatile approach to characterize a wide class of driven-dissipative systems. Published by the American Physical Society 2024

Screening of FDA-approved Drugs against Protein Thymidine Kinase 2 Using Machine Learning Method Validated Applying Molecular Dynamics and Free Energy Landscape Calculation

Background: Thymidine kinase 2 (TK2) is a crucial enzyme in the mitochondrial pyrimidine salvage pathway, strongly associated with several mitochondrial diseases. Current treatments frequently damage mitochondria, leading to a decrease in cellular energy output. Objective: This study aimed to use computational approaches to identify inhibitors of TK2 that could prevent these harmful consequences. Method: The initial screening process entailed the application of machine learning algorithms, more particularly a Random Forest model, which was trained on 189 FDA-approved drugs and decoy datasets obtained from the DUD-E database. Its purpose was to identify potential inhibitors. The molecular docking technique was employed to evaluate the affinity of the chosen medicines towards TK2. Molecular dynamics (MD) simulations lasting 100 nanoseconds were employed to conduct additional validation by examining the dynamic interactions between the top-found compounds and TK2. Results: Three hit compounds (3168, 5209502, and 135402009) were identified through the screening process for their high affinity for TK2. Compound 5209502 had the most stable interaction with the lowest root-mean-square deviation (RMSD) in the molecular dynamics (MD) simulations and maintained 12 hydrogen bonds consistently. MM/GBSA computations verified that 5209502 exhibited the strongest binding affinity with a binding free energy of -62.14 kcal/mol, which was notably lower than that of the control ligand. Conclusion: Compound 5209502 is a promising candidate for additional experimental evaluation because of its notable stability and great affinity for TK2. This chemical may provide a focused and less harmful treatment for mitochondrial illnesses linked to TK2 malfunction.

Identification of novel brain penetrant GSK-3β inhibitors toward Alzheimer’s disease therapy by virtual screening, molecular docking, dynamic simulation, and MMPBSA analysis

One of the significant therapeutic targets for Alzheimer’s disease (AD) is Glycogen Synthase Kinase-3β (GSK-3β). Inhibition of GSK-3β can prevent hyperphosphorylation of tau, and thus prevent formation and accumulation of neurofibrillary tangles and neuropil threads that block intracellular transport, trigger unfolded protein response, and increase oxidative stress, cumulatively leading to neurodegeneration. In this study, we have performed structure-based virtual screening of two small-molecule libraries from ChemDiv CNS databases using AutoDock Vina to identify hit molecules based on their binding affinities compared to that of an established GSK-3β inhibitor, indirubin-3’-monoxime (IMO). Pharmacoinformatic screening on SwissADME and pkCSM servers enabled identification of lead molecules with favorable pharmacoinformatic properties for drug likeliness, including blood brain barrier (BBB) permeability. Further, molecular dynamic simulations identified six candidate lead molecules that show stable complex formation with GSK-3β in dynamic state under physiological conditions. Principal component analysis of the dynamic state was used to plot Free Energy Landscapes (FELs) of GSK-3β-ligand complexes. STRIDE secondary structure analysis of the lowest energy conformations identified from FEL plots, and binding free energy calculations by Molecular Mechanics Poisson-Boltzmann Surface Area ((ΔGbind )MM-PBSA) of the simulation trajectories led to the identification of two ligands as potential lead molecules having favorable free energy landscape profiles as well as significantly lower (ΔGbind )MM-PBSA in dynamic state compared to that of reference inhibitor IMO. Hence, this study identifies two novel brain penetrant GSK-3β inhibitors that are likely to have therapeutic potential against Alzheimer’s disease.

Infrared Spectroscopy and Photochemistry of Ethyl Maltol in Low-Temperature Argon Matrix

Ethyl maltol was investigated using matrix isolation infrared spectroscopy and DFT calculations. In an argon matrix (14.5 K), the compound was found to exist in a single conformer (form I), characterized by an intramolecular hydrogen bond with an estimated energy of ~17 kJ mol−1. The IR spectrum of this conformer was assigned, and the molecule’s potential energy landscape was explored to understand the relative stability and isomerization dynamics of the conformers. Upon annealing the matrix to 41.5 K, ethyl maltol was found to predominantly aggregate into a centrosymmetric dimer (2× conformer I) bearing two intermolecular hydrogen bonds with an estimated energy of ca. 28 kJ mol−1 (per bond). The UV-induced (λ > 235 nm) photochemistry of the matrix-isolated ethyl maltol was also investigated. After 1 min of irradiation, band markers of two rearrangement photoproducts formed through the photoinduced detachment-attachment (PIDA) mechanism, in which the ethyl maltol radical acts as an intermediate, were observed: 1-ethyl-3-hydroxy-6-oxibicyclo [3.1.0] hex-3-en-2-one and 2-ethyl-2H-pyran-3,4-dione. The first undergoes subsequent reactions, rearranging to 4-hydroxy-4-propanoylcyclobut-2-en-1-one and photofragmenting to cyclopropenone and 2-hydroxybut-1-en-1-one. Other final products were also observed, specifically acetylene and CO (the expected fragmentation products of cyclopropenone), and CO2. Overall, the study demonstrated ethyl maltol’s high reactivity under UV irradiation, with significant photochemical conversion occurring within minutes. The rapid photochemical conversion, with complete consumption of the compound in 20 min, should be taken into account in designing practical applications of ethyl maltol.

Efficient energy management and cost optimization using multi-objective grey wolf optimization for EV charging/discharging in microgrid

The depletion of conventional energy sources coupled with the rising demand for electric vehicles (EVs) has significantly underscored the necessity for electric vehicle supply equipment (EVSE) with advanced energy supply management within microgrids. Effective energy management of EVs and EVSEs is imperative to satisfy EV owners and stabilize microgrids. This paper introduces multi-objective grey wolf optimization (MOGWO) to optimize EVSE and EV charging costs. MOGWO achieves substantial cost savings through dynamic pricing based on time of day, state-of-charge and hour-based scheduling which promotes off-peak charging thereby enhancing system efficiency and reducing costs. The algorithm also provides flexibility for trip interruptions and outperforms other optimization algorithms in minimizing EV charging/discharging costs by achieving reductions of up to ₹488 and obtaining average grid efficiency up to 76.41 % during charging and 87.41 % during discharging. Integrating Vehicle-to-Grid and Grid-to-Vehicle systems enhances microgrid resilience and delivers economic benefits to EV owners. The cost optimization for EV charging/discharging has been executed using MATLAB 2023a software to determine the most cost-effective charging paths by considering energy availability and grid conditions. The evolving sustainable energy landscape combined with MOGWO paves the way for smarter and more resilient microgrids in the era of electrified transportation.

Inertial effect of cell state velocity on the quiescence-proliferation fate decision

Energy landscapes can provide intuitive depictions of population heterogeneity and dynamics. However, it is unclear whether individual cell behavior, hypothesized to be determined by initial position and noise, is faithfully recapitulated. Using the p21-/Cdk2-dependent quiescence-proliferation decision in breast cancer dormancy as a testbed, we examined single-cell dynamics on the landscape when perturbed by hypoxia, a dormancy-inducing stress. Combining trajectory-based energy landscape generation with single-cell time-lapse microscopy, we found that a combination of initial position and velocity on a p21/Cdk2 landscape, but not position alone, was required to explain the observed cell fate heterogeneity under hypoxia. This is likely due to additional cell state information such as epigenetic features and/or other species encoded in velocity but missing in instantaneous position determined by p21 and Cdk2 levels alone. Here, velocity dependence manifested as inertia: cells with higher cell cycle velocities prior to hypoxia continued progressing along the cell cycle under hypoxia, resisting the change in landscape towards cell cycle exit. Such inertial effects may markedly influence cell fate trajectories in tumors and other dynamically changing microenvironments where cell state transitions are governed by coordination across several biochemical species.

Innovations in Wind Turbine Blade Engineering: Exploring Materials, Sustainability, and Market Dynamics

This manuscript delves into the transformative advancements in wind turbine blade technology, emphasizing the integration of innovative materials, dynamic aerodynamic designs, and sustainable manufacturing practices. Through an exploration of the evolution from traditional materials to cutting-edge composites, the paper highlights how these developments significantly enhance the efficiency, durability, and environmental compatibility of wind turbines. Detailed case studies of notable global projects, such as the Hornsea Project One, the Gansu Wind Farm, and the Block Island Wind Farm, illustrate the practical applications of these technologies and their impact on energy production and sustainability. Additionally, the manuscript examines the critical role of regulatory frameworks and industry standards in fostering these technological advancements, ensuring safety, and promoting global adoption. By analyzing the current trends and future directions, this study underscores the potential of modern turbine technologies to meet the increasing global demand for renewable energy and contribute to sustainable development goals. The findings advocate for continued innovation and policy alignment to fully harness the potential of wind energy in the renewable energy landscape.

Artificial Intelligence-Driven Regional Energy Transition:Evidence from China

As Artificial Intelligence (AI) technology advances rapidly, its role in promoting regional energy transformation is becoming increasingly apparent. This paper utilizes regional-level panel data from China, covering the period from 2011 to 2021, to systematically assess the impact of AI development on energy transformation. Using econometric models, including fixed-effects and instrumental variable regressions, the study reveals that an increase in AI enterprises within a region significantly reduces energy consumption per unit of GDP, thereby accelerating regional energy transition. The analysis identifies two primary channels through which AI exerts this effect: by facilitating the upgrading of the regional industrial structure and by promoting the growth of the digital economy. The findings also show that the impact of AI is more pronounced in highly urbanized regions, particularly in the Yangtze River Economic Belt. Additionally, the results highlight that AI-driven reductions in energy consumption are largely achieved through improved efficiency in coal and electricity usage, addressing key structural issues in China's energy landscape.

Driving sustainable energy transitions with a multi-source RAG-LLM system

By 2035, the UK aims to upgrade all homes to achieve a net-zero economy by 2050, thereby reducing energy consumption, household costs, and improving living conditions. Small and Medium-sized Enterprises (SMEs) play a crucial role in this transition. However, many SME contractors lack essential information on Sustainable Energy Initiatives (SEIs) and the relevant Energy landscape necessary for driving Sustainable Energy Transitions (SETs). This knowledge gap poses risks to SME interventions, potentially leading to increased costs and inefficiencies. Accessing timely information on SEIs including government policies, funding, technologies, and environmental impacts from various media sources is essential for guiding effective SETs and understanding the relevant Energy landscape, thereby facilitating informed decision-making. Currently, SMEs lack an integrated system that consolidates data from diverse media sources into a centralized Information System (IS), limiting their ability to effectively navigate SEIs. To address this gap, this research introduces an Energy Chatbot, a sustainable IS that utilizes Large Language Models (LLMs) integrated with multi-source Retrieval Augmented Generation (RAG). This system encompasses diverse media sources, including news articles, government reports, industry publications, academic research, and social media. The Energy Chatbot is designed to enhance decision-making for SMEs by providing comprehensive Energy sector insights through a Question Answering (QA) system. Key findings emphasize that this approach reduces costs by utilizing open-source models. Moreover, the Energy Chatbot provides SMEs with access to up-to-date information, enabling them to identify long-term sustainability strategies and maintain a competitive edge in the evolving Energy landscape.

Simulating irregular symmetry breaking in gut cross sections using a novel energy-optimization approach in growth-elasticity

Growth-elasticity (also known as morphoelasticity) is a powerful model framework for understanding complex shape development in soft biological tissues. At each instant, by mapping how continuum building blocks have grown geometrically and how they respond elastically to the push-and-pull from their neighbors, the shape of the growing structure is determined from a state of mechanical equilibrium. As mechanical loads continue to be added to the system through growth, many interesting shapes, such as smooth wavy wrinkles, sharp creases, and deep folds, can form on the tissue surface from a relatively flatter geometry.Previous numerical simulations of growth-elasticity have reproduced many interesting shapes resembling those observed in reality, such as the foldings on mammalian brains and guts. In the case of mammalian guts, it has been shown that wavy wrinkles, deep folds, and sharp creases on the interior organ surface can be simulated even under a simple assumption of isotropic uniform growth in the interior layer of the organ. Interestingly, the simulated patterns are all regular along the tube’s circumference, with either all smooth or all sharp indentations, whereas some undulation patterns in reality exhibit irregular patterns and a mixture of sharp creases and smooth indentations along the circumference. Can we simulate irregular indentation patterns without further complicating the growth patterning?In this paper, we have discovered abundant shape solutions with irregular indentation patterns by developing a Rayleigh–Ritz finite-element method (FEM). In contrast to previous Galerkin FEMs, which solve the weak formulation of the mechanical-equilibrium equations, the new method formulates an optimization problem for the discretized energy functional, whose critical points are equivalent to solutions obtained by solving the mechanical-equilibrium equations. This new method is more robust than previous methods. Specifically, it does not require the initial guess to be near a solution to achieve convergence, and it allows control over the direction of numerical iterates across the energy landscape. This approach enables the capture of more solutions that cannot be easily reached by previous methods. In addition to the previously found regular smooth and non-smooth configurations, we have identified a new transitional irregular smooth shape, new shapes with a mixture of smooth and non-smooth surface indentations, and a variety of irregular patterns with different numbers of creases. Our numerical results demonstrate that growth-elasticity modeling can match more shape patterns observed in reality than previously thought.