Free Energy
Articles tagged with Free Energy
Hybrid nonlinear generator broadens low-speed wind energy harvesting for self-powered devices
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.
Stabilized hybrid photocatalyst boosts artificial photosynthesis efficiency
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.
A new thermoelectric material to convert waste heat to electricity
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.
Researchers mimic a mystery of nature to make ice move on its own
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.
Breakthrough in materials science: AI reveals secrets of dendritic growth in thin films
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: Revolutionizing drug discovery with precise energy calculations
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.
Can consciousness exist in a computer simulation?
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.
Mathematical theory predicts self-organized learning in real neurons
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.
Explainable AI-based physical theory for advanced materials design
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.
SWEET RESEARCH: CHEMISTS UNLOCK SECRETS OF MOLTEN SALTS
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.
Partition function zeros are ‘shortcut’ to thermodynamic calculations on quantum computers
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.
Faster drug discovery through machine learning
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.
New physical picture leads to a precise finite-size scaling of (3+1)-dimensional O(n) critical system
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.
Mathematical modeling revealed how chitinase, a molecular monorail, obeys a one-way sign
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
Researcher invents an easy-to-use technique to measure the hydrophobicity of micro- and nanoparticle
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.
A new way to measure energy in microscopic machines
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.
Exploring the mystery of how enzymes work via simulations
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...
Reconstructing folding funnels from experimental data to uncover proteins' inner life
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.
Plenaries at American Chemical Society meeting will focus on computers in chemistry
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.
NYU chemists develop 'looking glass' for spotting sound molecular structures
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.
High-throughput cell-sorting method can separate 10 billion bacterial cells in 30 minutes
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.
When calculating cell-growth thermodynamics, reconsider using the Gibbs free energy equation
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.
A model-free way to characterize polymodal ion channel gating
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.
Folding funnels key to biomimicry
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.
Forcing the molecular bond issue
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.
Why do dew drops do what they do on leaves?
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.
Protein pulling -- Learning how proteins fold by pulling them apart
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.