Articles tagged with Free Energy
Researchers at UT San Antonio have found a way to create tiny clusters of disordered atoms that move oxygen ions more easily at lower temperatures, boosting fuel cell efficiency and durability. The breakthrough could help make fuel cells cheaper, more durable and easier to use outside the lab.
Researchers propose a data-driven forecasting approach to predict DER adoption patterns and quantify uncertainty for more informed decision making. The framework provides plausible adoption scenarios, enabling utilities, regulators, and planners to make strategic investments and plan for long-term infrastructure needs.
The Resilient Energy Technology and Infrastructure Consortium will develop innovative ways to meet the nation's growing energy needs, generating 21,000 jobs and $1 billion in economic growth. The consortium will focus on advanced manufacturing, AI, cybersecurity, and energy technology.
Lanzhou Jiaotong University researchers developed a droplet-based energy harvesting technology that converts secondary wastewater effluents into electricity. The system achieved high output performance and successfully powered LED lights, demonstrating its practical energy harvesting capability.
A new international study confirms that erosion plays a key role in forming and accumulating natural hydrogen in mountain ranges. The Pyrenees and Alps are identified as key targets for natural hydrogen exploration, with the right conditions allowing for efficient serpentinization and hydrogen production.
Researchers developed a memory technology that stores data using almost no electricity by controlling spin states through temperature changes. The approach reduces energy consumption by up to 66 times compared to existing methods, making it suitable for low-power memory technologies.
Researchers developed a nonlinear galloping-driven triboelectric-electromagnetic hybrid generator to harvest low-speed wind energy. The system can work over a wide wind-speed range and produce enough power to support practical electronics.
Researchers develop hybrid photocatalyst system to overcome light-induced damage in molecular catalysts, significantly improving CO2-to-formate quantum yield from 6% to over 27%. The new design ensures selective excitation of semiconductors and prevents unwanted photochemical reactions.
Researchers have discovered a new thermoelectric material, MoSi2, that can convert waste heat into electricity with high efficiency. The material's unique electronic structure and axis-dependent conduction polarity enable it to generate transverse thermopower, paving the way for efficient waste heat recovery systems.
Scientists at Virginia Tech mimic the natural movement of boulders on Racetrack Playa by creating a metal surface with asymmetric grooves that propel melting ice. The discovery has potential applications in rapid defrosting and energy harvesting.
A new AI model developed by Tokyo University of Science's researchers predicts dendritic growth in thin films, offering a powerful pathway for optimizing thin-film fabrication. The model analyzes morphology using persistent homology and machine learning with energy analysis, revealing conditions that drive branching behavior.
PairMap overcomes limitations of traditional methods by introducing intermediate compounds to predict binding affinities with high accuracy. The approach minimizes calculation errors, improves convergence, and reduces computational costs for complex transformations.
Wanja Wiese's research focuses on ruling out deception by conscious AI systems and understanding the prerequisites for consciousness in artificial systems. He draws on Karl Friston's free energy principle, suggesting that computers can simulate consciousness but may require additional conditions to replicate conscious experience.
Researchers used a mathematical theory called the free energy principle to predict how real neural networks learn and organize themselves. The study successfully mimicked this process in rat embryo neurons grown in a culture dish, demonstrating the principle's guiding force behind biological neural network learning.
Scientists at Tokyo University of Science developed an 'extended Landau free energy model' to analyze complex interactions in nanomagnetic devices, enabling causal analysis and visualization. The model proposed optimal structures for nano-devices with low power consumption.
Researchers have developed a novel simulation method to calculate free energy using deep learning artificial intelligence, providing accurate models of molten salts' thermodynamic properties. The study could help examine corrosion in metal containers and improve the design of next-generation nuclear reactors.
Researchers at North Carolina State University developed a new method to calculate thermodynamic properties using partition function zeros on quantum computers. By calculating the zeros of the partition function, they can determine free energy, entropy, and other properties without necessitating huge numbers of quantum computations.
DeepBAR, a new machine learning-based technique, quickly calculates binding affinities between drug candidates and their targets, offering a faster and more efficient approach to drug discovery and protein engineering. The approach yields precise calculations nearly 50 times faster than previous methods.
Researchers established an explicit scaling form for the free energy density, including a Gaussian fixed point and multiplicative logarithmic corrections. Monte Carlo simulations supported the conjectured scaling forms for various macroscopic quantities.
A team of researchers has developed a computational model to describe the motion of molecular motors, shedding light on how they generate unidirectional motion. The study found that molecular motors follow a
A groundbreaking method allows for easy determination of the surface free energy of particles, a quantitative measure of particle hydrophobicity. This innovation has significant implications for scientific and industrial applications involving particulate matter.
Researchers have devised a new method to measure free energy in microscopic systems, enabling the study of living systems and machine operation. The Relaxation Fluctuation Spectroscopy (ReFlucS) technique uses microscopy to track molecular motion, predicting system behavior without tracking individual atoms.
Enzymes play a crucial role in most biological processes by controlling energy transduction and genetic information. Researchers at USC determined that dynamics has little to do with accelerating enzyme-catalyzed reaction rates, clarifying the factors contributing to their activity. This discovery sheds light on the 100-year-old puzzle...
Scientists have developed a method to infer protein folding landscapes directly from experimental data, providing new insights into the structure-function relationship. This breakthrough uses nonlinear machine learning and statistical thermodynamics to reconstruct the folding funnels of proteins.
The plenary talks will illustrate the wide variety of applications for computers in science, including developing potent anti-HIV agents and creating new proteins. The presentations will also discuss recent advances in free energy perturbation theory.
Researchers created algorithm to identify key features of complex molecular structures, predicting optimal configurations and stability. This advancement could enhance production of pharmaceuticals, LED materials, and other products.
A new high-throughput cell-sorting method developed by Yi Zuo can separate 10 billion bacterial cells in just 30 minutes. The method uses surface free energy to sort cells, which could have direct applications for studying bacterial cells, microalgae, and other microbial samples.
A scientist suggests an alternative approach to calculating microbial growth thermodynamics, citing discrepancies between theoretical and experimental results. The Battley free energy equation offers a more realistic representation of real-world conditions.
Researchers develop thermodynamically rigorous analysis to parse free energy of polymodal voltage- and ligand-dependent ion channels. This new approach offers a model-independent way to study ion channel gating, useful for constraining future atomic-scale models and understanding disruptions caused by genetic mutations.
Researchers at Berkeley Lab discovered that protein-folding funnels can also apply to self-assembly of multiple proteins. The findings provide important guidelines for future biomimicry efforts, particularly in device fabrication and nanoscale synthesis.
Researchers developed a comprehensive model to describe molecular bonding, enabling predictions of binding free energy and resolving past inconsistencies. The new model provides a clear means for measuring this key parameter, critical for understanding material interactions.
A new study explains why dew drops form on leaf tips, rather than flat surfaces, based on the principle of free energy. Dew droplets tend to accumulate at the tips of spindly leaves due to their inherent 'unwillingness' to move on dry surfaces.
Scientists have developed a novel approach to probing protein folding energy, revealing the slope and height of the energy barrier proteins must overcome. This method has the potential to shed light on how amino acid sequences affect protein function and how diseases arise from misfolding.