Reductive Decomposition Research Articles

Degradation Effects in Li4Ti5O12-Based Cells─Learning from Electrode Potential Profiles.

Li4Ti5O12 (LTO) is a promising negative electrode active material for high-power applications of lithium-ion batteries due to its structural and dimensional stability, high safety properties, and rate capability. However, there are still challenges regarding the cyclic aging behavior of LTO-based cells, especially when it comes to the origin of gas evolution that needs to be resolved in order to enable an optimized cell design and prolonged cycle life. Using a three-electrode setup provides operando insights into the cyclic aging behavior of LiNi1/3Co1/3Mn1/3O2 (NCM111)||LTO cells. The results demonstrated that an initial slight increase in specific capacity followed by an intense decrease after 40 cycles could be attributed to a relative shift of the individual electrode potential profiles caused by loss of lithium inventory and associated electrolyte consumption during cyclic aging. By using higher formation temperatures, the capacity decrease, an associated loss of lithium inventory, and continuous electrolyte consumption were successfully suppressed. Moreover, the results suggested that gas evolution in Li4Ti5O12-based cells majorly originates from the reductive decomposition of moisture residues and the electrolyte at the Li4Ti5O12 electrode surface.

Enhanced MRI brain tumor detection and classification via topological data analysis and low-rank tensor decomposition

The advent of artificial intelligence in medical imaging has paved the way for significant advancements in the diagnosis of brain tumors. This study presents a novel ensemble approach that uses magnetic resonance imaging (MRI) to identify and categorize common brain cancers, such as pituitary, meningioma, and glioma. The proposed workflow is composed of a two-fold approach: firstly, it employs non-trivial image enhancement techniques in data preprocessing, low-rank Tucker decomposition for dimensionality reduction, and machine learning (ML) classifiers to detect and predict the type of brain tumor. Secondly, persistent homology (PH), a topological data analysis (TDA) technique, is exploited to extract potential critical areas in MRI scans. When paired with the ML classifier output, this additional information can help domain experts to identify areas of interest that might contain tumor signatures, improving the interpretability of ML predictions. When compared to automated diagnoses, this transparency adds another level of confidence and is essential for clinical acceptance. The performance of the system was quantitatively evaluated on a well-known MRI dataset, with an overall classification accuracy of 97.28% using an extremely randomized trees model. The promising results show that the integration of TDA, ML, and low-rank approximation methods is a successful approach for brain tumor identification and categorization, providing a solid foundation for further study and clinical application.

Soundness unknotted: An efficient soundness checking algorithm for arbitrary cyclic process models by loosening loops

Although domain experts usually create business process models, these models can still contain errors. For this reason, research and practice establish criteria for process models to provide confidence in the correctness or correct behavior of processes. One widespread criterion is soundness, which guarantees the absence of deadlocks and lacks of synchronization. Checking soundness of process models is not trivial. However, cyclic process models additionally increase the complexity to check soundness. This paper presents a novel approach for verifying soundness that has an efficient cubic worst-case runtime behavior, even for arbitrary cyclic process models. This approach relies on three key techniques — loop conversion, loop reduction, and loop decomposition — to convert any cyclic process model into a set of acyclic process models. Using this approach, we have developed five straightforward rules to verify the soundness, reusing existing approaches for checking soundness of acyclic models.

Mechanistic Analysis of Lithium-Air Battery with Organic Redox Mediator-Coated Air-Electrode

Redox mediators (RMs) suppress the charging overpotential to enhance the cycle performance of lithium-air batteries (LABs), but inappropriate RM incorporation can adversely shorten cycle life. In this study, three typical organic RMs; tetrathiafulvalene (TTF), 2,2,6,6-tetramethylpiperidine 1-oxyl (TEMPO), and 10-methylphenothiazine (MPT), were incorporated into the air-electrode (AE) of the LAB (RM-on-AE), rather than dissolving them in the electrolyte (RM-in-EL), to maximize the RM effect throughout the cycle life. The discharge/charge cycle test confirmed that the cells with RM-on-AE prevented the reductive decomposition of RM with the lithium anode, deriving the RM effect for a longer cycle life than the cells with RM-in-EL. The measurement of AE deposits revealed that the TTF- and TEMPO-on-AE cells failed to generate a quantitative amount of Li2O2 discharge product. In contrast, the MPT-on-AE provided a 96% yield of Li2O2 after the first discharge because of the reductive tolerance of the MPT as organic RM. The quantitative analysis also revealed an accumulation of Li2CO3 on the AEs, along with the generation of carboxylate, as the side products of irrelevant battery reactions. This study provides a practical methodology for selecting RMs and their incorporation for developing long-life LABs.

Photo-Energized MoS2/CNT Cathode for High-Performance Li–CO2 Batteries in a Wide-Temperature Range

HighlightsThe unique layered structure and excellent photoelectric properties of MoS2 facilitate the abundant generation and rapid transfer of photo-excited carriers, which accelerate the CO2 reduction and Li2CO3 decomposition upon illumination.MoS2-based photo-energized Li–CO2 battery displays ultra-low charge voltage of 3.27 V, high energy efficiency of 90.2%, superior cycling stability after 120 cycles and high rate capability.The low-temperature Li–CO2 battery achieves an ultra-low charge voltage of 3.4 V at –30 °C with a round-trip efficiency of 86.6%.

Cobalt oxyhydroxide nanoflakes enable ratiometric fluorescent assay of gallic acid

Gallic acid (GA) is widely used in beverages, food, and other fields as antioxidant. However, GA is slightly toxic and the accumulation of GA is harmful to human body. Therefore, it's vital to develop simple and sensitive detection methods for GA. In this work, a novel ratiometric fluorescent nanoprobe (named CoOOH/OPD/SiNPs) for the GA detection in different foods was designed and prepared. The fluorescence of silicon nanoparticles (SiNPs) at 443 nm would be quenched by cobalt oxyhydroxide (CoOOH) nanoflakes. o-phenylenediamine (OPD) would be oxidized to 2,3-diaminophenazine (DAP) by CoOOH nanoflakes that have peroxidase-like activity, which produces a new fluorescent peak at 556 nm. Meanwhile, SiNPs' fluorescence would be quenched through DAP due to inner filter effect (IFE). With the addition of GA, the reductive decomposition of CoOOH decreased DAP level, causing IFE being restrained. The concentration of GA indicates an excellent linear relationship with fluorescence ratio (F443/F556) in range of 0.4–12 μM (R2 = 0.9937) with 0.16 μM detection limit. This nanoprobe is applied to GA detection in water, tea leaves, fruits and nut fruits, which would be expected to act as a portable device for complex substances analysis.

Alloying and Segregation in PdRe/Al2O3 Bimetallic Catalysts for Selective Hydrogenation of Furfural

X-ray absorption fine structure (XAFS) spectroscopy, temperature-programmed reduction (TPR), and temperature-programmed hydride decomposition (TPHD) were employed to elucidate the structures of a series of PdRe/Al2O3 bimetallic catalysts for the selective hydrogenation of furfural. TPR evidenced low-temperature Re reduction in the bimetallic catalysts consistent of the migration of [ReO4]− (perrhenate) species to hydrogen-covered Pd nanoparticles on highly hydroxylated γ-Al2O3. TPHD revealed a strong suppression of β-PdHx formation in the reduced catalysts prepared by (i) co-impregnation and (ii) [HReO4] impregnation of the reduced Pd/Al2O3, indicating the formation of Pd-rich alloy nanoparticles; however, reduced catalysts prepared by (iii) [Pd(NH3)4]2+ impregnation of calcined Re/Al2O3 and subsequent re-calcination did not. Re LIII X-ray absorption edge shifts were used to determine the average Re oxidation states after reduction at 400 °C. XAFS spectroscopy and high-angle annular dark field (HAADF)-scanning transmission electron microscopy (STEM) revealed that a reduced 5 wt.% Re/Al2O3 catalyst contained small Re clusters and nanoparticles comprising Re atoms in low positive oxidation states (~1.5+) and incompletely reduced Re species (primarily Re4+). XAFS spectroscopy of the bimetallic catalysts evidenced Pd-Re bonding consistent with Pd-rich alloy formation. The Pd and Re total first-shell coordination numbers suggest that either Re is segregated to the surface (and Pd to the core) of alloy nanoparticles and/or segregated Pd nanoparticles are larger than Re nanoparticles (or clusters). The Cowley short-range order parameters are strongly positive indicating a high degree of heterogeneity (clustering or segregation of metal atoms) in these bimetallic catalysts. Catalysts prepared using the Pd(NH3)4[ReO4]2 double complex salt (DCS) exhibit greater Pd-Re intermixing but remain heterogeneous on the atomic scale.

Energy storage battery state of health estimation based on singular value decomposition for noise reduction and improved LSTM neural network

Accurate estimation of the State of Health (SOH) of batteries is important for intelligent battery management in energy storage systems. To solve the problems of poor quality of data features as well as the difficulty of model parameter adjustment, this study proposes a method for estimating the SOH of lithium batteries based on denoising battery health features and an improved Long Short-Term Memory (LSTM) neural network. First, in this study, three health features related to SOH decrease were selected from the battery charge/discharge data, and the singular value decomposition technique was applied to the noise reduction of the features to improve their correlation with the SOH. Then, the whale optimization algorithm is improved using cubic chaotic mapping to enhance its global optimization-seeking capability. Then, the Improved Whale Optimization Algorithm (IWOA) is used to optimize the model parameters of LSTM, and the IWOA-LSTM model is applied to the battery SOH estimation. Finally, the model proposed in this research is validated against the Center for Advanced Life Cycle Engineering (CALCE) battery dataset. The experimental results show that the prediction error of battery SOH by the method proposed in this study is less than 0.96%, and the prediction error is reduced by 49.42% compared to its baseline model. The method presented in the article achieves accurate estimation of the SOH, providing a reference for practical engineering applications.

Optimizing the Lattice Nitrogen Coordination to Break the Performance Limitation of Metal Nitrides for Electrocatalytic Nitrogen Reduction.

Metal nitrides (MNs) are attracting enormous attention in the electrocatalytic nitrogen reduction reaction (NRR) because of their rich lattice nitrogen (Nlat) and the unique ability of Nlat vacancies to activate N2. However, continuing controversy exists on whether MNs are catalytically active for NRR or produce NH3 via the reductive decomposition of Nlat without N2 activation in the in situ electrochemical conditions, let alone the rational design of high-performance MN catalysts. Herein, we focus on the common rocksalt-type MN(100) catalysts and establish a quantitative theoretical framework based on the first-principles microkinetic simulations to resolve these puzzles. The results show that the Mars-van Krevelen mechanism is kinetically more favorable to drive the NRR on a majority of MNs, in which Nlat plays a pivotal role in achieving the Volmer process and N2 activation. In terms of stability, activity, and selectivity, we find that MN(100) with moderate formation energy of Nlat vacancy (E vac) can achieve maximum activity and maintain electrochemical stability, while low- or high-E vac ones are either unstable or catalytically less active. Unfortunately, owing to the five-coordinate structural feature of Nlat on rocksalt-type MN(100), this maximum activity is limited to a yield of NH3 of only ∼10-15 mol s-1 cm-2. Intriguingly, we identify a volcano-type activity-regulating role of the local structural features of Nlat and show that the four-coordinate Nlat can exhibit optimal activity and overcome the performance limitation, while less coordinated Nlat fails. This work provides, arguably for the first time, an in-depth theoretical insight into the activity and stability paradox of MNs for NRR and underlines the importance of reaction kinetic assessment in comparison with the prevailing simple thermodynamic analysis.

Non-Flammable Electrolyte for Large-Scale Ni-Rich Li-Ion Batteries: Reducing Thermal Runaway Risks

This research explores the properties and applications of flame-retardant esters (FREs), specifically triethyl phosphate (TEP) and tributyl phosphate (TBP), within electrolyte systems. The investigation assesses their roles as additives (30%v/v) and primary solvents (80%v/v) in conjunction with carbonate-based electrolytes (1.0 M LiPF6 in EC:DEC:EMC) and a novel non-flammable electrolyte formulation (1.2 M LiPF6 in 70%v/v TEP with 30%v/v fluoroethylene carbonate, FEC). Results indicate that incorporating FREs as additives adversely affects cell performance and maintain flammability risks, necessitating alternative strategies for achieving nonflammability. However, utilizing FREs as primary solvents alongside carbonate solvents shows promise for developing non-flammable electrolytes, despite concerns such as reductive decomposition that may limit suitability for certain battery cells. Conversely, the incorporation of 70%v/v FREs with 30%v/v FEC in the new electrolyte formulation yields a stable solid electrolyte interface, inhibiting FRE decomposition and sustaining battery cycling performance. This combination offers a potential solution for producing non-flammable electrolytes, serving as a viable alternative to solid-state electrolytes. The compatibility of the 1.2 M LiPF6 in TEP:FEC, 70:30 %v/v formulation with existing battery manufacturing processes further enhances its applicability. Figure 1

Tuned Reactivity at the Lithium Metal – Argyrodite Solid State Electrolyte Interphase

Thin intermetallic Li2Te–LiTe3 bilayer (0.75 mm) derived from 2D tellurene stabilizes solid electrolyte interphase (SEI) of lithium metal and argyrodite (LPSCl, Li6PS5Cl) solid-state electrolyte (SSE). Tellurene is loaded onto standard battery separator and reacted with lithium through single-pass mechanical rolling, or transferred directly to SSE surface by pressing. State-of-the-art electrochemical performance is achieved, e.g. symmetric cell stable for 300 cycles (1800 hours) at 1 mA cm-2 and 3 mAh cm-2 (25% DOD, 60 mm foil). Cryo-FIB sectioning and Raman mapping demonstrate that Li2Te–LiTe3 bilayer impedes SSE decomposition. The unmodified Li–LPSCl interphase is electrochemically unstable with geometrically heterogeneous reduction decomposition reaction front that extends deep into the SSE. Decomposition drives voiding in Li metal due to its high flux to the reaction front, as well as voiding in the SSE due to the associated volume changes. Analysis of cycled SSEs found no evidence for pristine (unreacted) lithium metal filaments/dendrites, implying failure driven by decomposition phases with sufficient electrical conductivity that span electrolyte thickness. Density Functional Theory (DFT) calculations clarify thermodynamic stability, interfacial adhesion, and electronic transport properties of interphases, while mesoscale modeling examines interrelations between reaction front heterogeneity (SEI heterogeneity), current distribution and localized chemo-mechanical stresses.

Monitoring the Solid-Electrolyte Interphase Formation in Bis(fluorosulfonyl)Amide Ionic Liquid Electrolyte Using a Redox Probe Method

The solid-electrolyte interphase (SEI) has been considered to be formed on the negative electrode surface by reductive decomposition of electrolyte components. In the case of the electrolytes containing lithium ions, the SEI can be regarded as the Li+ conductor that enables the negative electrode reactions and prevents the further decomposition of the electrolytes. The structure of the SEI is often explained by the mosaic model, in which the SEI is a composite of several different inorganic and organic domains [1]. On the other hand, we have proposed the formation of the gelled SEI in bis(fluorosulfonyl)amide (FSA–) ionic liquid electrolytes containing a high concentration of LiFSA [2,3]. Furthermore, we have reported that the SEI formation can be detected by monitoring the redox reaction of ferrocene (Fc) and ferrocenium (Fc+) [4,5]. In the present study, the time-dependent transition of the SEI formed on a Pt electrode was investigated by the redox probe method in BMPFSA (BMP+ = 1-butyl-1-methylpyrrolidinium) containing LiFSA.The formation of SEI by holding a Pt electrode at a negative potential was confirmed by a shift in the anodic peak potential of the cyclic voltammogram for Fc+/Fc couple in LiFSA/BMPFSA. The anodic peak disappeared when the electrode was held at the negative potential for a few hours, indicating the formation of a thick SEI. However, the anodic peak was observed again after keeping the electrode at the open circuit potential for a few hours, indicating the disappearance of the SEI. These results suggest that the SEI is formed by dissolution and/or dispersion of the decomposed products, which form at the electrode surface and diffuse into the electrolyte. Thus, the SEI formed at the potential is considered to exist as a metastable phase in the balance between the formation and diffusion of the decomposition products. This work was partly supported by GteX Program Japan Grant Number JPMJGX23S0.[1] E. Peled, D. Golodnitsky, and G. Ardel, J. Electrochem. Soc., 144, L208 (1997).[2] R. Furuya, T. Hara, T. Fukunaga, K. Kawakami, N. Serizawa, and Y. Katayama, J. Electrochem. Soc., 168, 100516 (2021).[3] N. Serizawa, R. Yamashita, and Y. Katayama, J. Phys. Chem. C, 127, 10434 (2023).[4] S. Kato, N. Serizawa, and Y. Katayama, J. Electrochem. Soc., 169, 076509 (2022).[5] S. Kato, N. Serizawa, and Y. Katayama, J. Electrochem. Soc., 170, 056504 (2023).

Continuous sepsis trajectory prediction using tensor-reduced physiological signals

The quick Sequential Organ Failure Assessment (qSOFA) system identifies an individual’s risk to progress to poor sepsis-related outcomes using minimal variables. We used Support Vector Machine, Learning Using Concave and Convex Kernels, and Random Forest to predict an increase in qSOFA score using electronic health record (EHR) data, electrocardiograms (ECG), and arterial line signals. We structured physiological signals data in a tensor format and used Canonical Polyadic/Parallel Factors (CP) decomposition for feature reduction. Random Forests trained on ECG data show improved performance after tensor decomposition for predictions in a 6-h time frame (AUROC 0.67 ± 0.06 compared to 0.57 ± 0.08, p=0.01\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p = 0.01$$\\end{document}). Adding arterial line features can also improve performance (AUROC 0.69 ± 0.07, p<0.01\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p < 0.01$$\\end{document}), and benefit from tensor decomposition (AUROC 0.71 ± 0.07, p=0.01\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p = 0.01$$\\end{document}). Adding EHR data features to a tensor-reduced signal model further improves performance (AUROC 0.77 ± 0.06, p<0.01\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p < 0.01$$\\end{document}). Despite reduction in performance going from an EHR data-informed model to a tensor-reduced waveform data model, the signals-informed model offers distinct advantages. The first is that predictions can be made on a continuous basis in real-time, and second is that these predictions are not limited by the availability of EHR data. Additionally, structuring the waveform features as a tensor conserves structural and temporal information that would otherwise be lost if the data were presented as flat vectors.

Macro-micro decomposition for consistent and conservative model order reduction of hyperbolic shallow water moment equations: a study using POD-Galerkin and dynamical low-rank approximation

Geophysical flow simulations using hyperbolic shallow water moment equations require an efficient discretization of a potentially large system of PDEs, the so-called moment system. This calls for tailored model order reduction techniques that allow for efficient and accurate simulations while guaranteeing physical properties like mass conservation. In this paper, we develop the first model reduction for the hyperbolic shallow water moment equations and achieve mass conservation. This is accomplished using a macro-micro decomposition of the model into a macroscopic (conservative) part and a microscopic (non-conservative) part with subsequent model reduction using either POD-Galerkin or dynamical low-rank approximation only on the microscopic (non-conservative) part. Numerical experiments showcase the performance of the new model reduction methods including high accuracy and fast computation times together with guaranteed conservation and consistency properties.

Two-dimensional boron-rich BxN as a potential electrode for synergistic immobilization and catalysis of lithium polysulfides: A first-principles investigation

Lithium-sulfur batteries show high promise as the next-generation energy storage systems due to their impressive energy density, cost-effectiveness, and environmentally friendly characteristics. However, the well-known shuttle effect and the sluggish conversion dynamics of lithium polysulfides (LiPSs) during discharging and charging processes hinder their practical application. Herein, we investigate the potential of two-dimensional boron-rich BxN (x=2, 3, and 5) as a viable additive to enhance the anchoring of soluble LiPSs and improve the LiPSs conversion dynamics by harnessing the flexible B-N cooperative interactions. Through density functional calculations, we demonstrate that these BxN electrodes exhibit desirable anchoring ability towards soluble lithium polysulfides (LiPSs) in a realistic solution environment by employing explicit solvent molecules in conjunction with an implicit solvent model. Furthermore, B3N and B5N exhibit robust electrocatalytic activity in both the sulfur reduction and LiPSs decomposition reactions. Intriguingly, a novel cross-sheet mechanism was discovered in B2N, characterized by an Ebarrier of 0.27, 0.20, 0.40, 0.37 and 0.49 eV for the decomposition of Li2S, Li2S2, Li2S4, Li2S6 and Li2S8, attributed to its distinctive configuration with a large eight-ring structure. Additionally, the introduction of BxN can significantly alleviates the volumetric changes experienced by the sulfur cathode during the charging and discharging processes. BxN is a promising catalyst for boosting the electrochemical conversion processes of LiPSs in Li-S batteries and exhibit desirable anchoring ability for inhibiting the shuttle effect induced by soluble LiPSs. These findings offer an alternative approach for the development of advanced sulfur cathode catalysts with reduced shuttle effects and improved rate performance.

Elucidating the Transport of Electrons and Molecules in a Solid Electrolyte Interphase Close to Battery Operation Potentials Using a Four-Electrode-Based Generator-Collector Setup.

In lithium-ion batteries, the solid electrolyte interphase (SEI) passivates the anode against reductive decomposition of the electrolyte but allows for electron transfer reactions between anode and redox shuttle molecules, which are added to the electrolyte as an internal overcharge protection. In order to elucidate the origin of these poorly understood passivation properties of the SEI with regard to different molecules, we used a four-electrode-based generator-collector setup to distinguish between electrolyte reduction current and the redox molecule (ferrocenium ion Fc+) reduction current at an SEI-covered glassy carbon electrode. The experiments were carried out in situ during potentiostatic SEI formation close to battery operation potentials. The measured generator and collector currents were used to calculate passivation factors of the SEI with regard to electrolyte reduction and with regard to Fc+ reduction. These passivation factors show huge differences in their absolute values and in their temporal evolution. By making simple assumptions about molecule transport, electron transport, and charge transfer reaction rates in the SEI, distinct passivation mechanisms are identified, strong indication is found for a transition during SEI growth from redox molecule reduction at the electrode | SEI interface to reduction at the SEI | electrolyte interface, and good estimates for the transport coefficients of both electrons and redox molecules are derived. The approach presented here is applicable to any type of electrochemical interphase and should thus also be of interest for interphase characterization in the fields of electrocatalysis and corrosion.

Influence of different stalks on the metallization degree of FeCl3-derived magnetic biochar through pyrolysis behavior and compositional differences

To investigate the effect of stalk type on the metallization degrees in FeCl3-derived magnetic biochar (MBC), MBC was synthesized via an impregnation-pyrolysis method using six different stalks. The Fe0 content in MBC significantly influenced its magnetic properties and ostensibly governed its catalytic capabilities. Analysis of the interaction between stalks and FeCl3 revealed that the variation in metallization degrees, resulting from FeCl2 decomposition (6.1%) and stalk-mediated reduction (20.7%), was directly responsible for the observed differences in MBC metallization. The presence of oxygen-containing functional groups and fixed carbon appeared to promote metallization in MBC induced by reduction. A series of statistical analyses indicated that the cellulose, lignin, and hemicellulose content of the stalks were key factors contributing to differences in MBC metallization degrees. Further exploration revealed that hemicellulose and cellulose were more effective than lignin in enhancing metallization through FeCl2 decomposition and reduction. Constructing stalk models demonstrated that the variance in the content of these three biomass components across the six stalk types could lead to differences in the metallization degree attributable to reduction and FeCl2 decomposition, thereby affecting the overall metallization degree of MBC. A prediction model for MBC metallization degree was developed based on these findings. Moreover, the elevated Si content in some stalks facilitated the formation of Fe2(SiO4), which subsequently impeded the reduction process. This study provides a theoretical foundation for the informed selection of stalk feedstocks in the production of FeCl3-derived MBC.

Inhibition of silicon-based anode interfacial volume expansion behavior by 1,3,6-hexane trinitrile additive via induced interfacial solvation effect

Silicon(Si)-based anodes exhibit higher energy density than traditional graphite anodes but suffer from significant volume changes during lithiation/delithiation, destabilizing the solid electrolyte interface (SEI) and reducing cycle stability. This study explores the effect of 1,3,6-hexane trinitrile (HTCN) on mitigating Si@C electrode expansion. The inclusion of HTCN modifies the original solvation structure of Li+, and the HTCN additive undergoes preferential coordination with Li+ because its binding energy with Li+ is larger than that of ethylene carbonate (EC). This lowers the lowest unoccupied molecular orbital (LUMO) of the HTCN-containing solvation structure, promoting preferential reduction decomposition on the anode surface. This process increases inorganic Li3N formation and produces a rich carbon-organic SEI film, effectively reducing electrolyte decomposition and Si@C anode expansion from 72.98 % to 4.46 %. Adding 1 % HTCN to Si@C/Li half-cells enhances capacity retention to 72.4 % after 100 cycles at 0.2C, significantly outperforming cells without HTCN (50.9 %). This study provides a new idea for constructing high-performance SEI films to inhibit electrode expansion by adjusting the solvation structure of Li+.

Bifunctional Ligand Enables Gold-Catalyzed Propargyl C-H Functionalization via Reactive Gold Allenylidene Intermediate.

Gold allenylidene species have been seldom exploited as reactive intermediates in synthetically versatile catalytic reactions. By employing alkynylbenziodoxoles as the substrates and bifunctional WangPhos as the metal ligand, this work demonstrated ready catalytic access to these intermediates of general substitution patterns and their electrophilic reactivities at the γ-carbon center with a diverse range of nucleophiles. The reaction is driven by the reductive decomposition of the benziodoxole moiety and achieves the replacement of a propargylic proton with an N/O/C-based nucleophile, hence realizing reactivity umpolung. Corroborated by Density Functional Theory (DFT) calculations, the reaction mechanism involves a mild propargylic deprotonation. In contrast to prior works employing a tertiary amine functionality, a weakly BrØnsted-basic amide group in WangPhos is surprisingly effective in deprotonation at the propargylic position under a gold-ligand cooperation regime.

Bifunctional Ligand Enables Gold‐Catalyzed Propargyl C−H Functionalization via Reactive Gold Allenylidene Intermediate

AbstractGold allenylidene species have been seldom exploited as reactive intermediates in synthetically versatile catalytic reactions. By employing alkynylbenziodoxoles as the substrates and bifunctional WangPhos as the metal ligand, this work demonstrated ready catalytic access to these intermediates of general substitution patterns and their electrophilic reactivities at the γ‐carbon center with a diverse range of nucleophiles. The reaction is driven by the reductive decomposition of the benziodoxole moiety and achieves the replacement of a propargylic proton with an N/O/C‐based nucleophile, hence realizing reactivity umpolung. Corroborated by Density Functional Theory (DFT) calculations, the reaction mechanism involves a mild propargylic deprotonation. In contrast to prior works employing a tertiary amine functionality, a weakly BrØnsted‐basic amide group in WangPhos is surprisingly effective in deprotonation at the propargylic position under a gold‐ligand cooperation regime.