Redox proteomics generates bounded biochemical measurements that are categorically mismatched to conventional linear algebraic formalisms. This work introduces Oxi-Shapes, a tropical geometric framework for the measurement-native analysis of bounded redox proteomic data. Oxi-Shapes represents cysteine oxidation as a scalar field over a discrete lattice, enabling global and site-wise analysis without rescaling, interpolation, or kinetic assumptions. At the global level, the framework yields internal redox entropy, lattice curvature, and derived energy functionals that characterise the geometric structure of the redox proteome. At the site level, Oxi-Shapes defines a bounded change space that makes explicit hard geometric constraints on admissible redox transitions and enables a normalised signed representation of site-wise change as a fraction of available redox freedom. Applied to an ageing mouse brain dataset, Oxi-Shapes reveals that a small decrease in mean oxidation arises from a profound redistribution of site-wise redox states, with thousands of residues shifting toward the reduced absorbing boundary. These results demonstrate that categorically correct algebraic representatio
Electrochemically redox-active halide (eREAL) materials are an emerging class of materials that combine high Li-ion conductivity with transition-metal redox activity, making them promising candidates for cathode or catholyte applications. As a redox-active catholyte, they could significantly increase the energy density of solid-state batteries. In this work, we perform first-principles calculations on Li-M-Cl (M = 3d transition metals) ternaries to establish such a theoretical foundation for their stability and electrochemical activity. We map the phase stability of eREAL structures with varying metal-to-Cl ratio, transition-metal species, oxidation states, and anion frameworks, and compute cation and anion redox potentials. We find that the high ionicity of metal-Cl bonds elevates cation redox potentials above those of conventional oxide cathodes, but also will promote Cl oxidation and Cl-Cl dimerization at high voltages, which may limit the stability of these materials. Anion substitution effectively tunes both cation and anion redox potentials, with F substitution standing out as a viable route to extend the reversible voltage window. Beyond the anion redox issue, eREAL compound
Redox biology underpins signalling, metabolism, immunity, and adaptation, yet lacks a unifying theoretical framework capable of formalising structure, function, and dynamics. Current interpretations rely on descriptive catalogues of molecules and reactions, obscuring how redox behaviour emerges from constrained biochemical organisation. Here, we present a mathematical theory of redox biology that resolves this gap by treating redox systems as finite, compositional, dynamical, and spatially embedded objects. We define a structured redox state space in which admissible molecular transformations form a neutral algebra of possibilities. Biological function emerges when this structure is embedded within a wider molecular network and interpreted through weighted flux distributions. Time-dependent reweighting of these transformations generates redox dynamics, while spatial embedding enforces locality and causality, yielding a distributed redox field. Within this framework, context dependence, nonlinearity, hysteresis, and memory arise naturally from bounded state spaces and irreversible transformations, without requiring ad hoc assumptions. This theory provides a working, predictive inter
Describing Li-ion battery positive electrodes in terms of distinct transition metal or oxygen redox regimes can lead to confusion in understanding metal-ligand hybridisation, oxygen dimerisation, and degradation. There is a pressing need to study the electronic structure of these materials and determine the role each cation and anion plays in charge compensation. Here, we employ transition metal L-edge X-ray Resonance Photoemission Spectroscopy in conjunction with Single Impurity Anderson models, Self-consistent Real Space Multiple Scattering spectral simulations, and Dynamical Mean-Field theory calculations to directly evaluate the redox mechanisms in (de-)lithiated battery electrodes. This approach reconciles the redox description of two canonical cathodes -- LiMn$_{0.6}$Fe$_{0.4}$PO$_{4}$ and LiNiO$_{2}$ -- in terms of varying degrees of charge transfer using the established Zaanen-Sawatzky-Allen framework, common to condensed matter physics. In LiMn$_{0.6}$Fe$_{0.4}$PO$_{4}$, the absence of charge transfer means capacity arises due to the depopulation of metal $\textit{3d}$ states, i.e. conventional metal redox. Whereas, in LiNiO$_{2}$, charge transfer dominates and redox occur
Based on detailed electronic structure and spectroscopy obtained using DFT-based many-body techniques, the redox behavior of Fe impurities in BaTiO$_3$ is investigated. It is observed that Fe impurities exhibit a mixed valence nature, comprising mostly Fe$^{2+}$ ($3d^6$) and Fe$^{3+}$ ($3d^5$) configurations, and such configurations can be tuned via oxygen vacancies which favor Fe$^{2+}$. The origin of such a redox behavior can be attributed to the charge transfer caused by shifting of the $d_{3z^2-r^2}$ orbitals. Furthermore, x-ray photoemission spectroscopy is evaluated by solving the Wannier function-derived local atomic Hamiltonian using the crystal field multiplet approach, with good agreements with recent experimental measurements.
Volatile outgassing from planetary interiors controls the composition of rocky exoplanets' secondary atmospheres. However, observations indicate that disequilibrium processes, such as photochemistry and vertical transport, can strongly alter the chemical structure of Hot Jupiters. Which process dominates under different types of rocky planets, and how outgassing and photochemistry jointly determine the atmospheric composition, remain open questions. Sulfur species are promising tracers of interior-atmosphere coupling because their atmospheric abundances are sensitive to both mantle redox state and stellar irradiation. The PROTEUS planetary interior-atmosphere evolution modelling framework is coupled to two chemical models, FastChem and VULCAN, for post-processed chemistry calculations. We run a grid of planetary evolution simulations spanning diverse mantle redox states, instellation fluxes, and Solar versus M-star host-star spectra. For each case, we compare atmospheric compositions under thermochemical equilibrium, only vertical transport, and vertical transport plus photochemistry. The bulk atmospheric composition remains controlled by the redox state of the mantle and outgassin
Numerous aqueous systems host elements in multiple redox states, with wide ranging implications such as their influence on the formation/dissolution of minerals, water toxicity, and nutrient cycling. To uncover governing mechanisms and complex chemical interactions in aqueous systems, reactive-transport models have increasingly gained importance. However, their predictive capabilities remain limited because existing approaches struggle to accurately account for the full complexities of redox reactions. Here, we develop a reactive-transport framework that leverages recent advancements in thermodynamic modelling, speciation chemistry, and redox kinetics. Distinct from traditional models, we uniquely treat redox kinetics along with transport as transient phenomena, decoupled from Gibbs free energy minimisation. Ensuring ion concentrations are governed by non-equilibrium rate laws, this approach allows predicting the tempo-spatial distribution of speciation and precipitation of species across oxidation states. We illustrate the versatility of our framework through two case studies: manganese speciation in natural waters and the fate of dissolved iron in aqueous/porous media. Our framew
Understanding how the electronic structure of electrodes influences electrocatalytic reactions has been a longstanding topic in the electrochemistry community, with predominant attention paid to metallic electrodes. In this work, we present a defect physics perspective on the effect of semiconductor band structure on electrochemical redox reactions. Specifically, the Haldane-Anderson model, originally developed to study multiple charge states of transition-metal defects in semiconductors, is extended to describe electrochemical redox reactions by incorporating the solvent effect, inspired by the Holstein model. The solvent coordinate and the actual charge on the redox species in reduced and oxidized states are assumed to be instant equilibrium, and the transitions between these states are defined by the framework of Green's function. With these treatments, we treat the charge state transition in a self-consistent manner. We first confirm that this self-consistent approach is essential to accurately depict the hybridization effect of band structure by comparing the model-calculated ionization potential (IP), electron affinity (EA), and redox potential of the species with those obtai
The participation of oxygen or other anionic species in redox activities in cathode materials for lithium and sodium-ion battery systems is known to play a role in governing the useful capacity of these batteries. Directly probing anionic redox mechanisms is not possible, rather the computational analysis by density functional theory poses the main approach towards gleaning insights into anionic redox activity and harnessing these effects to maximize capacity in future electrode materials. Here we showcase material systems exhibiting this mechanism of ion insertion and removal, and present the key computational considerations in studying anionic redox activities in battery materials. Aided by new computationally derived understandings of the role of anionic redox in emerging battery materials, increasingly greater levels of useable capacities can be extracted through informed materials design.
Capture of anthropogenic CO2 is critical for mitigating climate change, and reducing the energy cost is essential for wide-scale deployment. Solubility of inorganic carbon in aqueous solutions depends on the pH, and electrochemical modulation of the pH has been investigated as a means of CO2 capture and release. However, reported methods incur unavoidable energy costs due to thermodynamic penalties. In this study, we introduce a pH-independent redox chemistry that greatly lowers the thermodynamic energy costs by changing the pH without directly changing the [H+]. We show that the redox reaction of TEMPO molecules modulates the pH for capture and release of CO2 in a flow cell with an energy cost as low as 2.6 kJ/mol of CO2 corresponding to 0.027 eV/molecule. A molecular model, supported by MD and DFT simulations, is proposed of how the pH is decreased by 7.6 while largely avoiding the entropic energy cost associated with increasing the [H+]. We believe that this work showcases the potential of pH-independent redox chemistries for practical and cost-effective CO2 capture.
This paper proposes Redox, a training data management system designed to achieve high I/O efficiency. The key insight is a new observation of file redirection: for model training, when training data in one file is requested, the system has the flexibility to return the data of another file. Based on this property, Redox starts with a bold design principle that chunks of data files are always read from disk in batch, and once loaded, all files in the chunk will be consumed without being loaded again. We propose efficient local and distributed file read protocol based on this principle that both minimizes the wasted data read and enables opportunistic prefetch from remote node. Moreover, we analyze file redirection's impact on randomness, and show that it has little effects on training efficiency. Experimental results indicate that Redox significantly accelerates data fetching in training, achieving up to a 4.57x improvement in end-to-end training compared to PyTorch.
A thermodynamic integration (TI) protocol incorporating dummy atoms is introduced to calculate free energy differences for disulfide bond formation in proteins. This method successfully reproduces experimental redox potentials for multiple proteins, providing improved insights into the redox regulation of various proteins.
Cyclic Voltammetry (CV) is the most commonly used method in electrochemistry to characterize electrochemical reactions, usually involving macroscopic electrodes. Here we demonstrate an optical CV technique called Opto-iontronic Microscopy, which is capable of monitoring electrochemical processes at the nanoscale. By integrating optical microscopy with nanohole electrodes, we enhance sensitivity in detecting redox reactions within volumes as small as an attoliter ($(100 \text{~nm})^{3}$). This technique uses total internal reflection illumination, Electric-double-layer modulation, cyclic voltammetry, and lock-in detection, to probe ion dynamics inside nanoholes. We applied this method to study EDL (dis)charging coupled to ferrocenedimethanol (Fc(MeOH)$_2$) redox reactions. Experimental results were validated against a theoretical Poisson-Nernst-Planck-Butler-Volmer model, providing insights into ion concentration changes of reaction species that contribute to the optical contrast. This work opens up opportunities for high-sensitivity, label-free analysis of electrochemical reactions in nanoconfined environments, with potential applications in pure nanocrystal growth and monitoring.
A chloroplast is a subcellular organelle of photosynthesis in plant and algal cells. A chloroplast genome encodes proteins of the photosynthetic electron transport chain and ribosomal proteins required to express them. Chloroplast-encoded photosynthetic proteins are mostly intrinsic to the chloroplast thylakoid membrane where they drive vectorial electron and proton transport. There they function in close contact with proteins whose precursors are encoded in the cell nucleus for cytosolic synthesis, subsequent processing, and import into the chloroplast. The protein complexes of photosynthetic electron transport thus contain subunits with one of two quite different sites of synthesis. If most chloroplast proteins result from expression of nuclear genes then why not all? What selective pressure accounts for the persistence of the chloroplast genome? One proposal is that photosynthetic electron transport itself governs expression of genes for its own components: co-location of chloroplast genes with their gene products allows redox regulation of gene expression, thereby resulting in self-adjustment of protein stoichiometry in response to environmental change. This hypothesis posits C
We develop a systems approach based on an energy-landscape concept to differentiate interactions involving redox activities and conformational changes of proteins and nucleic acids interactions in multi-layered protein-DNA regulatory networks under light disturbance. Our approach is a data-driven modeling workflow using a physics-informed machine learning algorithm to train a non-linear mathematical model for interpreting gene expression dynamics and to lead discovery for protein regulators using redox proteome analysis. We distinguish light-responsive elements within central carbon metabolism pathways from independent variables like circadian time using the publicly available transcriptome datasets of Synechococcus elongatus over diel cycles responding to light perturbations. Our approach provides interpretable de novo models for elucidating events of reactions in complex regulatory pathways in response to stressful disturbance from the environment. We discovered protein regulators in response to light disturbance in the proteome analysis involving shifts in protein abundance as well as cysteine redox states under constant illumination and after two hours of darkness. We discovere
Machine learning potentials (MLPs) represent atomic interactions with quantum mechanical accuracy offering an efficient tool for atomistic simulations in many fields of science. However, most MLPs rely on local atomic energies without information about the global composition of the system. To date, this has prevented the application of MLPs to redox reactions in solution, which involve chemical species in different oxidation states and electron transfer between them. Here, we show that fourth-generation MLPs overcome this limitation and can provide a physically correct description of redox chemical reactions. For the example of ferrous (Fe$^{2+}$) and ferric (Fe$^{3+}$) ions in water we show that the correct oxidation states are obtained matching the number of chloride counter ions irrespective of their positions in the system. Moreover, we demonstrate that our method can describe electron-transfer processes between ferrous and ferric ions, paving the way to simulations of general redox chemistry in solution.
The tunable design of protein redox potentials promises to open a range of applications in biotechnology and catalysis. Here we introduce a method to calculate redox potential changes by combining fluctuation relations with molecular dynamics simulations. It involves the simulation of reduced and oxidized states, followed by the instantaneous conversion between them. Energy differences introduced by the perturbations are obtained using the Kubo-Onsager approach. Using a detailed fluctuation relation coupled with Bayesian inference, these are post-processed into estimates for the redox potentials in an efficient manner. This new method, denoted MD+CB, is tested on a de novo four-helix bundle heme protein (the m4D2 `maquette') and five designed mutants, including some mutants characterized experimentally in this work. The MD+CB approach is found to perform reliably, giving redox potential shifts with reasonably good correlation (0.85) to the experimental values for the mutants. The MD+CB approach also compares well with redox potential shift predictions using a continuum electrostatic method. The estimation method employed within the MD+CB approach is straightforwardly transferable t
Interactions between magma oceans and overlying atmospheres on young rocky planets leads to an evolving feedback of outgassing, greenhouse forcing, and mantle melt fraction. Previous studies have predominantly focused on the solidification of oxidized Earth-similar planets, but the diversity in mean density and irradiation observed in the low-mass exoplanet census motivate exploration of strongly varying geochemical scenarios. We aim to explore how variable redox properties alter the duration of magma ocean solidification, the equilibrium thermodynamic state, melt fraction of the mantle, and atmospheric composition. We develop a 1D coupled interior-atmosphere model that can simulate the time-evolution of lava planets. This is applied across a grid of fixed redox states, orbital separations, hydrogen endowments, and C/H ratios around a Sun-like star. The composition of these atmospheres is highly variable before and during solidification. The evolutionary path of an Earth-like planet at 1 AU ranges between permanent magma ocean states and solidification within 1 Myr. Recently solidified planets typically host H2O- or H2-dominated atmospheres in the absence of escape. Orbital separat
Redox potentials of electron transfer reactions are of fundamental importance for the performance and description of electrochemical devices. Despite decades of research, accurate computational predictions for the redox potential of even simple metals remain very challenging. Here we use a combination of first principles calculations and machine learning to predict the redox potentials of three redox couples, $\mathrm{Fe}^{2+}$/$\mathrm{Fe}^{3+}$, $\mathrm{Cu}^{+}$/$\mathrm{Cu}^{2+}$ and $\mathrm{Ag}^{+}$/$\mathrm{Ag}^{2+}$. Using a hybrid functional with a fraction of 25\% exact exchange (PBE0) the predicted values are 0.92, 0.26 and 1.99 V in good agreement with the best experimental estimates (0.77, 0.15, 1.98 V). We explain in detail, how we combine machine learning, thermodynamic integration from machine learning to semi-local functionals, as well as a combination of thermodynamic perturbation theory and $Δ$-machine learning to determine the redox potentials for computationally expensive hybrid functionals. The combination of these approaches allows one to obtain statistically accurate results.
Gas and moisture sensing devices leveraging the resistive switching effect in transition metal oxide memristors promise to revolutionize next-generation, nano-scaled, cost-effective, and environmentally sustainable sensor solutions. These sensors encode readouts in resistance state changes based on gas concentration, yet their nonlinear current-voltage characteristics offer richer dynamics, capturing detailed information about REDOX reactions and surface kinetics. Traditional vertical devices fail to fully exploit this complexity. This study demonstrates planar resistive switching devices, moving beyond the Butler-Volmer model. A systematic investigation of the electrochemical processes in Na-doped ZnO with lateral planar contacts reveals intricate patterns resulting from REDOX reactions on the device surface. When combined with advanced algorithms for pattern recognition, allow the analysis of complex switching patterns, including crossings, loop directions, and resistance values, providing unprecedented insights for next-generation complex sensors.