Minimum Free Energy Research Articles

Synthetic biology approach revealed enhancement in haeme oxygenase-1 gene expression by codon pair optimization while reduction by codon deoptimization.

Haem oxygenase-1 (HO-1) is a ubiquitously expressed gene involved in cellular homoeostasis, and its imbalance in expression results in various disorders. To alleviate such disorders, HO-1 gene expression needs to be modulated. Codon usage bias results from evolutionary forces acting on any nucleotide sequence and determines the gene expression. Like codon usage bias, codon pair bias also exists, playing a role in gene expression. In the present study, HO-1 gene was recoded by manipulating codon and codon pair bias, and four such constructs were made through codon/codon pair deoptimization and codon/codon pair optimization to reduce and enhance the HO-1 gene expression. Codon usage analysis was done for these constructs for four tissues brain, heart, pancreas and liver. Based on codon usage in different tissues, gene expression of these tissues was determined in terms of the codon adaptation index. Based on the codon adaptation index, minimum free energy, and translation efficiency, constructs were evaluated for enhanced or decreased HO-1 expression. The analysis revealed that for enhancing gene expression, codon pair optimization, while for reducing gene expression, codon deoptimization is efficacious. The recoded constructs developed in the study could be used in gene therapy regimens to cure HO-1 over or underexpression-associated disorders.

The mechanism and energetics of the dynein priming stroke

Dyneins are an AAA+ motor responsible for motility and force generation toward the minus end of microtubules. Dynein motility is powered by nucleotide-dependent transitions of its linker domain, which transitions between straight (post-powerstroke) and bent (pre-powerstroke) conformations. To understand the dynamics and energetics of the linker, we performed all-atom molecular dynamics simulations of human dynein-2 primed for its power stroke. Simulations revealed that the linker can adopt either a bent conformation or a semi-bent conformation, separated by a 5.7 kT energy barrier. The linker cannot switch back to its straight conformation in the pre-powerstroke state due to a steric clash with the AAA+ ring. Simulations also showed that an isolated linker has a free energy minimum near the semi-bent conformation in the absence ofthe AAA+ ring, indicating that the linker stores energy as it bends and releases this energy during the powerstroke.

Molecular insights into codon usage analysis of mitochondrial fission and fusion gene: relevance to neurodegenerative diseases.

Mitochondrial dysfunction is the leading cause of neurodegenerative disorders like Alzheimer's disease and Parkinson's disease. Mitochondria is a highly dynamic organelle continuously undergoing the process of fission and fusion for even distribution of components and maintaining proper shape, number, and bioenergetic functionality. A set of genes governs the process of fission and fusion. OPA1, Mfn1, and Mfn2 govern fusion, while Drp1, Fis1, MIEF1, and MIEF2 genes control fission. Determination of specific molecular patterns of transcripts of these genes revealed the impact of compositional constraints on selecting optimal codons. AGA and CCA codons were over-represented, and CCC, GTC, TTC, GGG, ACG were under-represented in the fusion gene set. In contrast, CTG was over-represented, and GCG, CCG, and TCG were under-represented in the fission gene set. Hydropathicity analysis revealed non-polar protein products of both fission and fusion gene set transcripts. AGA codon repeats are an integral part of translational regulation machinery and present a distinct pattern of over-representation and under-representation in different transcripts within the gene sets, suggestive of selective translational force precisely controlling the occurrence of the codon. Out of six synonymous codons, five synonymous codons encoding for leucine were used differently in both gene sets. Hence, forces regulating the occurrence of AGA and five synonymous leucine-encoding codons suggest translational selection. A correlation of mutational bias with gene expression and codon bias and GRAVY and AROMA signifies the selection pressure in both gene sets, while the correlation of compositional bias with gene expression, codon bias, protein properties, and minimum free energy signifies the presence of compositional constraints. More than 25% of codons of both gene sets showed a significant difference in codon usage. The overall analysis shed light on molecular features of gene sets involved in fission and fusion.

Multi-targeted therapeutic potential of stigmasterol from the Euphorbia ammak plant in treating lung and breast cancer

Cancer is the most prevalent disease globally, which presents a significant challenge to the healthcare industry, with breast and lung cancer being predominant malignancies. This study used RNA-seq data from the TCGA database to identify potential biomarkers for lung and breast cancer. Tumor Necrosis Factor (TNFAIP8) and Sulfite Oxidase (SUOX) showed significant expression variation and were selected for further study using structure-based drug discovery (SBDD). Compounds derived from the Euphorbia ammak plant were selected for in-silico study with both TNFAIP8 and SUOX. Stigmasterol had the greatest binding scores (normalized scores of −8.53 kcal/mol and −9.69 kcal/mol) with both proteins, indicating strong stability in their binding pockets throughout the molecular dynamics’ simulation. Although Stigmasterol first changed its initial conformation (RMSD = 0.5 nm with the starting conformation) in SUOX, it eventually reached a stable conformation (RMSD of 1.5 nm). The compound on TNFAIP8 showed a persistent shape (RMSD of 0.35 nm), indicating strong protein stability. The binding free energy of the complex was calculated using the MM/GBSA technique; TNFAIP8 had a ΔGTOTAL of −24.98 kcal/mol, with TYR160 being the most significant residue, contributing −2.52 kcal/mol. On the other hand, the SUOX complex had a binding free energy of −16.87 kcal/mol, with LEU151 being the primary contributor (-1.17 kcal/mol). Analysis of the complexes' free energy landscape unveiled several states with minimum free energy, indicating robust interactions between the protein and ligand. In its conclusion, this work emphasises the favourable ability of Stigmasterol to bind with prospective targets for lung and breast cancer, indicating the need for more experimental study.

DERNA Enables Pareto Optimal RNA Design.

The design of an RNA sequence that encodes an input target protein sequence is a crucial aspect of messenger RNA (mRNA) vaccine development. There are an exponential number of possible RNA sequences for a single target protein due to codon degeneracy. These potential RNA sequences can assume various secondary structure conformations, each with distinct minimum free energy (MFE), impacting thermodynamic stability and mRNA half-life. Furthermore, the presence of species-specific codon usage bias, quantified by the codon adaptation index (CAI), plays a vital role in translation efficiency. While earlier studies focused on optimizing either MFE or CAI, recent research has underscored the advantages of simultaneously optimizing both objectives. However, optimizing one objective comes at the expense of the other. In this work, we present the Pareto Optimal RNA Design problem, aiming to identify the set of Pareto optimal solutions for which no alternative solutions exist that exhibit better MFE and CAI values. Our algorithm DEsign RNA (DERNA) uses the weighted sum method to enumerate the Pareto front by optimizing convex combinations of both objectives. We use dynamic programming to solve each convex combination in time and space. Compared with a CDSfold, previous approach that only optimizes MFE, we show on a benchmark data set that DERNA obtains solutions with identical MFE but superior CAI. Moreover, we show that DERNA matches the performance in terms of solution quality of LinearDesign, a recent approach that similarly seeks to balance MFE and CAI. We conclude by demonstrating our method's potential for mRNA vaccine design for the SARS-CoV-2 spike protein.

Preferential ordering of incommensurate-length guest particles in a smectic host.

Using density functional theory, we study the preferential ordering of rod-like guest particles immersed in a smectic host fluid. Within a model of perfectly aligned rods and assuming that the guest particles do not perturb the smectic host fluid, simple excluded-volume arguments explain that guest particles that are comparable in length to the host particles order in phase with the smectic host density layering, whereas guest particles that are considerably shorter or longer order in antiphase. The corresponding free-energy minima are separated by energetic barriers on the order of the thermal energy kBT, suggesting that guest particles undergo hopping-type diffusion between adjacent smectic layers. Upon introducing a slight orientational mismatch between the guest particles and the perfectly aligned smectic host, an additional, smaller free-energy barrier emerges for a range of intermediate guest-to-host length ratios, which splits the free-energy minimum into two. Guest particles in this range occupy positions intermediate between in-phase ordering and in-antiphase ordering. Finally, we use Kramers' theory to identify slow, fast, and intermediate diffusive regimes for the guest particles as a function of their length. Our model is in qualitative agreement with experiment and simulation and provides an alternative, complementary explanation in terms of a free-energy landscape for the intermediate diffusive regime, which was previously hypothesized to result from temporary caging effects [M. Chiappini, E. Grelet, and M. Dijkstra, Phys. Rev. Lett. 124, 087801 (2020)]. We argue that our simple model of aligned rods captures the salient features of incommensurate-length guest particles in a smectic host if a slight orientational mismatch is introduced.

Wetting Behavior of Inkjet-Printed Electronic Inks on Patterned Substrates.

In inkjet printing technology, one important factor influencing the printing quality and reliability of printed films is the interaction of the jetted ink with the substrate surface. This short-range interaction determines the wettability and the adhesion of the ink to the solid surface and is hence responsible for the final shape of the deposited ink. Here, we investigate wetting morphologies of inkjet-printed inks on patterned substrates by carefully designed experimental test structures and simulations. The contact angles, the surface properties, and drop shapes, as well as their influence on the device variability, are experimentally and theoretically analyzed. For the simulations, we employ the phase-field method, which is based on the free energy minimization of the two-phase system with the given wetting boundary conditions. Through a systematic investigation of printed drops on patterned substrates consisting of hydrophilic and hydrophobic areas, we report that the printed morphology is related not only to the designed layout and the drop volume but also to the printing strategy and the wettability. Furthermore, we show how one can modify the intrinsic wettability of the patterned substrates to enhance the printing quality and reliability. Based on the present findings, we cast light on the improvement of the fabrication quality of thin film transistors.

The dependence of electrocatalytic HER activity of decorated MoS2 with Cu nanoclusters

Molybdenum disulfide is a potential electrocatalytic material for water splitting with excellent stability and durability. Here, the electrocatalytic hydrogen evolution reaction performance of Molybdenum disulfide nanosheets decorated with copper nanoclusters was investigated. The Cu@MoS2–1:10 exhibited remarkable hydrogen evolution reaction activity, with an overpotential as low as 150 mV under acidic conditions. The First-principles calculations further confirmed the superior electrocatalytic performance of Cu@MoS2, with a minimum hydrogen adsorption free energy of -0.10 eV. The improved electrocatalytic activity of Cu@MoS2 arises from the accelerated charge migration at the interface. And the addition of Cu increases the number of active sites on the surface of MoS2. This work could offer insights into the advancement of additional non-precious metals and transition metal dichalcogenides.

Understanding thermodynamic stability and carbothermal reduction in SiOC

This study explores the phase evolution, carbothermal reduction, and energetics in SiOaCb derived from the pyrolysis of trimethylsilyl terminated polyhydromethylsiloxane (PHMS-TMS) from 1200 to 1600 °C. High-resolution X-ray photoelectron spectroscopy (XPS) shows notable differences in the constituent bond arrangement between the more Si-rich SiOC ceramics and the more C-rich SiOC ceramics, which subsequently affects the microstructure of the SiOC systems. Carbothermal reduction starts at lower temperatures than previously reported and indicates no phase separation of SiOC or formation of SiO2 nanodomains. The thermodynamic stability of the thus-obtained SiOC ceramics is evaluated using a Gibbs free energy minimization method. The results suggest that differences in silicon mixed bonding environments and their relative amounts (SiO2C2, SiOC3, or SiC4) lead to differences in thermodynamic stability.

Surface-Accelerated String Method for Locating Minimum Free Energy Paths.

We present a surface-accelerated string method (SASM) to efficiently optimize low-dimensional reaction pathways from the sampling performed with expensive quantum mechanical/molecular mechanical (QM/MM) Hamiltonians. The SASM accelerates the convergence of the path using the aggregate sampling obtained from the current and previous string iterations, whereas approaches like the string method in collective variables (SMCV) or the modified string method in collective variables (MSMCV) update the path only from the sampling obtained from the current iteration. Furthermore, the SASM decouples the number of images used to perform sampling from the number of synthetic images used to represent the path. The path is optimized on the current best estimate of the free energy surface obtained from all available sampling, and the proposed set of new simulations is not restricted to being located along the optimized path. Instead, the umbrella potential placement is chosen to extend the range of the free energy surface and improve the quality of the free energy estimates near the path. In this manner, the SASM is shown to improve the exploration for a minimum free energy pathway in regions where the free energy surface is relatively flat. Furthermore, it improves the quality of the free energy profile when the string is discretized with too few images. We compare the SASM, SMCV, and MSMCV using 3 QM/MM applications: a ribozyme methyltransferase reaction using 2 reaction coordinates, the 2'-O-transphosphorylation reaction of Hammerhead ribozyme using 3 reaction coordinates, and a tautomeric reaction in B-DNA using 5 reaction coordinates. We show that SASM converges the paths using roughly 3 times less sampling than the SMCV and MSMCV methods. All three algorithms have been implemented in the FE-ToolKit package made freely available.

On the Thomas–Fermi model: Gabor J. Kalman's contribution and numerical approximations

AbstractIn this article, we would like to pay tribute to Gabor Kalman, outlining his contribution to a model widely used in dense plasma physics: the high‐temperature Thomas–Fermi model. The approach of Ruoxian Ying and Kalman relies on the separation of the bound and free electrons, a physically reasonable definition of the bound electrons, a description of the source density in the Poisson equation through the electron–ion and ion–ion pair correlation functions and a determination of the degree of ionization from the minimization of the total free energy. We also report on different approximations of the function , which is a cornerstone of the original Thomas‐Femi model.

Ligand Lipophilicity Determines Molecular Mechanisms of Nanoparticle Adsorption to Lipid Bilayers.

The interactions of ligand-functionalized nanoparticles with the cell membrane affect cellular uptake, cytotoxicity, and related behaviors, but relating these interactions to ligand properties remains challenging. In this work, we perform coarse-grained molecular dynamics simulations to study how the adsorption of ligand-functionalized cationic gold nanoparticles (NPs) to a single-component lipid bilayer (as a model cell membrane) is influenced by ligand end group lipophilicity. A set of 2 nm diameter NPs, each coated with a monolayer of organic ligands that differ only in their end groups, was simulated to mimic NPs recently studied experimentally. Metadynamics calculations were performed to determine key features of the free energy landscape for adsorption as a function of the distance of the NP from the bilayer and the number of NP-lipid contacts. These simulations revealed that NP adsorption is thermodynamically favorable for all NPs due to the extraction of lipids from the bilayer and into the NP monolayer. To resolve ligand-dependent differences in adsorption behavior, string method calculations were performed to compute minimum free energy pathways for adsorption. These calculations revealed a surprising nonmonotonic dependence of the free energy barrier for adsorption on ligand end group lipophilicity. Large free energy barriers are predicted for the least lipophilic end groups because favorable NP-lipid contacts are initiated only through the unfavorable protrusion of lipid tail groups out of the bilayer. The smallest free energy barriers are predicted for end groups of intermediate lipophilicity which promote NP-lipid contacts by intercalating within the bilayer. Unexpectedly, large free energy barriers are also predicted for the most lipophilic end groups which remain sequestered within the ligand monolayer rather than intercalating within the bilayer. These trends are broadly in agreement with past experimental measurements and reveal how subtle variations in ligand lipophilicity dictate adsorption mechanisms and associated kinetics by influencing the interplay of lipid-ligand interactions.

Asymmetric membrane "sticky tape" enables simultaneous relaxation of area and curvature in simulation.

Biological lipid membranes are generally asymmetric, not only with respect to the composition of the two membrane leaflets but also with respect to the state of mechanical stress on the two sides. Computer simulations of such asymmetric membranes pose unique challenges with respect to the choice of boundary conditions and ensemble in which such simulations are to be carried out. Here, we demonstrate an alternative to the usual choice of fully periodic boundary conditions: The membrane is only periodic in one direction, with free edges running parallel to the single direction of periodicity. In order to maintain bilayer asymmetry under these conditions, nanoscale "sticky tapes" are adhered to the membrane edges in order to prevent lipid flip-flop across the otherwise open edge. In such semi-periodic simulations, the bilayer is free to choose both its area and mean curvature, allowing for minimization of the bilayer elastic free energy. We implement these principles in a highly coarse-grained model and show how even the simplest examples of such simulations can reveal useful membrane elastic properties, such as the location of the monolayer neutral surface.

Monocular 3D object detection with thermodynamic loss and decoupled instance depth

Monocular 3D detection is to obtain the 3D information of the object from the image. The mainstream methods mainly use L1 loss or L1-like loss to control the instance depth prediction. However, these methods have not achieved satisfactory results. One of the main reasons is that L1 loss or L1-like loss does not accurately reflect the fit between the predicted instance depth and the corresponding ground truth. Another of the main reason is that the instance depth on the RGB image hard to be directly learned in the network. In order to solve the above problems, a novel thermodynamic loss based on the principle of free energy minimisation and a novel depth decoupling method are proposed in this paper. The proposed method is called the monocular 3D object detection network with thermodynamic loss and decoupled instance depth (TDN). In TDN, the optimisation of the instance depth prediction is regarded as the thermodynamic process. Therefore, the thermodynamic loss is designed according to the principle of free energy minimisation. TDN decouples the instance depth into three different depths. By combining the thermodynamic loss and the different types of depths, we can obtain the final instance depth.

Fine-tuning activation specificity of G-protein-coupled receptors via automated path searching

Physics-based simulation methods can grant atomistic insights into the molecular origin of the function of biomolecules. However, the potential of such approaches has been hindered by their low efficiency, including in the design of selective agonists where simulations of myriad protein-ligand combinations are necessary. Here, we describe an automated input-free path searching protocol that offers (within 14 d using Graphics Processing Unit servers) a minimum free energy path (MFEP) defined in high-dimension configurational space for activating sphingosine-1-phosphate receptors (S1PRs) by arbitrary ligands. The free energy distributions along the MFEP for four distinct ligands and three S1PRs reached a remarkable agreement with Bioluminescence Resonance Energy Transfer (BRET) measurements of G-protein dissociation. In particular, the revealed transition state structures pointed out toward two S1PR3 residues F263/I284, that dictate the preference of existing agonists CBP307 and BAF312 on S1PR1/5. Swapping these residues between S1PR1 and S1PR3 reversed their response to the two agonists in BRET assays. These results inspired us to design improved agonists with both strong polar head and bulky hydrophobic tail for higher selectivity on S1PR1. Through merely three in silico iterations, our tool predicted a unique compound scaffold. BRET assays confirmed that both chiral forms activate S1PR1 at nanomolar concentration, 1 to 2 orders of magnitude less than those for S1PR3/5. Collectively, these results signify the promise of our approach in fine agonist design for G-protein-coupled receptors.

Anisotropic Magnetic Polymeric Particles with a Controllable Structure via Seeded Emulsion Polymerization.

Magnetic polymer composites have been widely utilized in potential applications in material science, such as reduction of dyes, immunodiagnostics, biomedicals, and magnetically controllable photonic crystals owing to large surface areas, fast separation, and recyclable performance. In this work, anisotropic magnetic particles were prepared by seeded emulsion polymerization, with morphologies of "Fe3O4-shell", "hemisphere-like", "raspberry-like", "multiple lobes-like", and "sandwich-like". Poly(styrene/divinylbenzene/mono-2-(methacryloxy)ethyl succinate)@ Fe3O4 (P(St/DVB/MMES)@Fe3O4) were the seed microspheres, and P(St/DVB/MMES)@Fe3O4@polymer particles are achieved by seeded emulsion polymerizations. The morphology of the particles depends on polymerization conditions (monomer ratios and surfactant), particle properties, and so on. Then, the minimum surface free energy change principles were used to predict the equilibrium morphologies of the magnetic polymer composites. Through theory, the model gives the correct tendency and good agreement with the equilibrium morphology which was in tandem with TEM results. Lastly, after in situ surface deposition of Ag nanoparticles, magnetic composite particles with sandwich-like morphology were applied for the catalytic degradation of 4-nitrophenol (4-NP) reacting with NaBH4. The apparent rate coefficient is 0.0069 s-1, and it can keep mainly about 80% efficiency in catalysis after five cycles.

The effect of oxygen fugacity on the evaporation of boron from aluminoborosilicate melt

Abstract. We present the results of B2O3 evaporation experiments from Ca- and Mg-bearing aluminoborosilicate melts. Our experiments were conducted at 1245 to 1249 ∘C and 1350 to 1361 ∘C for different run times (60–1020 min), and at oxygen fugacities (logfO2) relative to the fayalite–magnetite–quartz (FMQ) buffer of FMQ−6 to FMQ+1.5, and in air. Our results show that with increasing fO2, evaporation of B from the melt increases by a factor of 5 compared to reducing conditions. Using Gibbs free energy minimization calculations, we suggest two possible evaporation reactions for B2O3 which constrain its speciation in the gas phase to be either 3+ or 4+ (B2O3(g) and BO2(g)). The measured B2O3 contents of the B evaporated residual glasses were used to calculate evaporation rate constants (ki) for B2O3 in oxidizing conditions (air, ki=2.09×10-4 cm min−1 at 1350 ∘C) and reducing conditions (FMQ−4, ki=4.46×10-5 cm min−1 at 1350 ∘C). The absence of diffusion profiles in the experimental glasses suggests that the evaporation rates are slower than B2O3 diffusion rates and therefore the rate-limiting process. Overall, the rate of B evaporation in air is approximately a factor of 5 higher compared to reducing conditions at FMQ−4.

A Neuro-Mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization

Over the last few years, large neural generative models, capable of synthesizing semantically rich passages of text or producing complex images, have recently emerged as a popular representation of what has come to be known as ``generative artificial intelligence'' (generative AI). Beyond opening the door to new opportunities as well as challenges for the domain of statistical machine learning, the rising popularity of generative AI brings with it interesting questions for Cognitive Science, which seeks to discover the nature of the processes that underpin minds and brains as well as to understand how such functionality might be acquired and instantianted in biological (or artificial) substrate. With this goal in mind, we argue that a promising research program lies in the crafting of cognitive architectures, a long-standing tradition of the field, cast fundamentally in terms of neuro-mimetic generative building blocks. Concretely, we discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition in terms of Hebbian adaptation operating in service of optimizing a variational free energy.

Structure Comparison of Beta Amyloid Peptide Aβ 1-42 Isoforms. Molecular Dynamics Modeling.

Beta amyloid peptide Aβ 1-42 (Aβ42) has a unique dual role in the human organism, as both the peptide with an important physiological function and one of the most toxic biological compounds provoking Alzheimer's disease (AD). There are several known Aβ42 isoforms that we discuss here that are highly neurotoxic and lead to the early onset of AD. Aβ42 is an intrinsically disordered protein with no experimentally solved structure under physiological conditions. The objective of this research was to establish the appropriate molecular dynamics (MD) methodology and model a uniform set of structures for the Aβ42 isoforms that form the core of this study. For that purpose, force field selection and verification including convergence testing for MD simulations was made. Replica exchange MD and conventional MD modeling of several Aβ42 and Aβ16 isoforms that have neurotoxic and amyloidogenic effects impacting the severity of Alzheimer's disease were carried out with the optimal force field and solvent parameters. A standardized ensemble of structures for the Aβ42 and Aβ16 isoforms covering 30-50% of the conformational ensembles extracted from the free energy minima was calculated from MD trajectories. The resulting data set of modeled structures includes Aβ42 wild type, isoD7, pS8, D7H, and H6R-Aβ42 and Aβ16 wild type, isoD7, pS8, D7H, and H6R-Aβ16. The representative structures are given in the Supporting Information; they are open for public access. In the study, we also evaluated the differences between the structures of Aβ42 isoforms and speculate on their possible relevance to the known functions. Utilizing several representative structures for a single disordered protein for docking, with their subsequent averaging by conformations, would markedly increase the reliability of docking results.

Thermodynamic and experimental study on carbothermal reduction of JSC-1A lunar regolith simulant for metal and metalloid production

The carbothermal reduction of JSC-1A lunar simulant with activated carbon was investigated for producing metals and metalloids. Chemical equilibrium compositions were examined using Gibb’s free energy minimization to identify favorable operating temperatures at 0.1, 10-8, and 3 × 10-15 bar for Fe and Si production. Complete conversion of Fe2O3 to Fe(g) was predicted at temperatures above 850 °C and about 90 % conversion of SiO2 to Si(g) was predicted at temperatures above 1000 °C for lunar surface pressure of 3 × 10-15 bar. Thermogravimetry was used to examine the reactions with mixtures of JSC-1A and stoichiometric activated carbon in 100 % Ar up to 1500 °C, and comparisons between temporal mass losses and CO evolution were used to estimate volatile production. The results were compared to ultra-high vacuum experiments at ∼ 10-12 bar with temporal CO measured. Solid-state surface material characterization was performed using scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray photoelectron spectroscopy, X-ray diffractometry, and transmission electron microscopy to identify elemental distributions and particle morphology of sample surface before and after experiment as well as chemical compositions present after experiment. Changes in elemental distributions and morphologies of Si-, Fe-, and Ti- in the sample before and after the experiment provided strong evidence of reactions of Si-, Fe-, and Ti- compounds in JSC-1A. Characterization of the loose sample after TGA confirmed the presence of crystalline SiC, Si, and Fe. Deposits collected on Cu foils mounted above the sample in the ultra-high vacuum experiments were found to contain Fe-, Al-, Mg-, and Si- volatiles.