Bulletin of the Korean Chemical Society最新文献

Enzyme-based biosensors with mediated electrochemical detection offer a straightforward and cost-effective approach for detecting xanthine. However, electro-active interfering species such as ascorbic acid (AA) complicate the achievement of sensitive and selective detection in biological fluids. Direct and mediated oxidation of AA elevates electrochemical background levels. While ascorbate oxidase (AOx) is employed to oxidize AA into an electro-inactive product, incomplete removal of AA allows it to reduce the electron mediator, resulting in still considerable background levels. Additionally, excess AOx can oxidize the signaling species, the reduced form of the electron mediator, albeit slowly, leading to decreased signal levels. To address these challenges, a two-step incubation process and the use of appropriate AOx concentration are implemented. Once AA is fully oxidized by AOx, an electron mediator is added to the solution. To enhance the electrochemical signal-to-background ratio, an optimal pairing of a xanthine-oxidizing enzyme and an electron mediator is selected from two xanthine-oxidizing enzymes [xanthine dehydrogenase (XDH) and xanthine oxidase] and three electron mediators [Os(bpy)₂Cl₂⁺, Ru(NH₃)₆³⁺, and Fe(CN)₆³⁻]. The combination of XDH and Os(bpy)₂Cl₂⁺ provides high signal and low background levels. When these conditions are applied to xanthine detection in artificial serum, a detection limit of approximately 500 nM is achieved, making it applicable in various clinical and research fields.

Targeted protein degradation (TPD) is a relatively novel drug discovery strategy that could help break through the limitations of traditional small molecule inhibitors. While TPD mostly utilizes diverse E3 ligases to incorporate the ubiquitin-proteasome system (UPS), cereblon (CRBN) could be considered one of the most successfully adopted E3 ligases. Thus, expanding the scope of CRBN ligands has received tremendous attention to overcome related issues, such as selectivity and druggability. In this study, design and synthesis of novel benzosultam-based CRBN ligands have been explored by replacement of lactam in lenalidomide with sultam. The sultam-based ligands showed CRBN binding affinities 2-20 times stronger than lenalidomide, presumably from additional hydrogen bonds generated from the extra oxygen atom in the sultam group, as supported by docking studies. This research highlights the potential of novel benzosultam CRBN ligands as a new tool for CRBN-mediated TPD strategies.

Although lithium (Li) metal is considered a next-generation material owing to its high theoretical capacity, several challenges restrict its use, such as rapid dendrite formation and continuous decomposition of the electrolyte at the interfaces. In this regard, we propose tris(trimethylsilyl) phosphite (TMSP) as an effective additive for stabilizing Li metal anodes in the presence of propylene carbonate (PC) solvent. Incorporating the TMSP additive into PC-based electrolytes provides a highly stable and robust interface at the Li anode because the TMSP additive facilitates the formation of LiSiO_x and Li_xPO_yF_z-based solid electrolyte interfaces (SEI) at the Li anode. These stable TMSP-derived SEI layers inhibit the uneven dendritic Li growth at the anode interface, effectively preventing further decomposition of the PC-based electrolyte. In Li/NCM622 cells, the TMSP-P-SE exhibited stable cycling retention after 100 cycles (72.3%), whereas the P-SE revealed a drastic decrease in cycling retention after only 10 cycles. These results indicate that the SEI formed on the Li anode in the presence of TMSP effectively inhibits parasitic reactions, thereby enhancing cycling retention significantly.

Solid-state NMR spectroscopy has gained increasing attention as a probe to investigate structures and dynamics of various solid materials, in particular, materials for rechargeable batteries. Cathodes and anodes for rechargeable batteries contain unpaired electrons (paramagnetic), which generate localized magnetic fields at molecules. Owing to this paramagnetic interaction, it is often complicated to measure and interpret the NMR spectra of the paramagnetic systems. Thus, understanding the effect of the interaction between unpaired electrons and nuclei is important. NMR spectroscopy at higher magnetic fields has been perceived as beneficial since this can provide higher sensitivity and resolution. However, the response to the magnetic field strength varies depending on the nuclei of interest and material properties because various factors affect NMR characteristics. In this work, we performed a systematic study of the effect of the field strength on the magic angle spinning (MAS) NMR characteristics by comparing diamagnetic and paramagnetic systems at two different magnetic fields. As diamagnetic materials, LiCoO₂ (LCO) and Li₂O are examined. As paramagnetic materials, LiFePO₄(LFP) and lithium nickel manganese cobalt oxides (LiNi_xMn_yCo_1–x–yO₂) with different compositions are investigated. We have demonstrated that higher signal intensity and narrower linewidths can be obtained at higher magnetic fields for diamagnetic systems, whereas higher signal intensity and better resolution are obtained at lower magnetic fields for paramagnetic systems. Our research will provide systematic and experimental evidence about the field strength dependence of paramagnetic systems and rationalized grounds for choosing proper NMR spectrometers for each material.

Accurate classification and authentication of edible oils are essential for maintaining product quality, ensuring consumer safety, and preserving market integrity. Therefore, this study aims to propose Fourier-transform infrared (FT-IR) spectroscopy, combined with advanced machine learning models, as a rapid and non-destructive technique for classifying edible oils. The FT-IR spectra of seven edible oil types were analyzed across three spectral regions: the full range, the C-H stretching range, and the fingerprint region. Both absorbance and second derivative spectra were used to evaluate the influence of spectral preprocessing on classification accuracy. Six machine learning models—principal component analysis followed by linear discriminant analysis (PCA-LDA), k-nearest neighbors, decision tree, random forest, eXtreme Gradient Boosting, and support vector machines (SVM)—were employed to classify the oils, achieving training accuracies of 96.4%–100% and testing accuracies of 88.1%–100%. The second derivative spectra enhanced model performance by improving the resolution of overlapping peaks, particularly in the CH and CO stretching regions. Additionally, the SHapley Additive exPlanations analysis further revealed the most critical spectral features influencing model predictions, offering valuable insights into the decision-making processes. This study demonstrates the effectiveness of combining FT-IR spectroscopy, second derivative preprocessing, and machine learning techniques for classifying edible oils. The findings highlight the benefits of second derivative spectra in enhancing spectral resolution and the superior classification performance of PCA-LDA and SVM models. These results offer a robust framework for advancing edible oil analysis and emphasize the potential of artificial intelligence in food authentication and quality control.

For the construction of a nanoparticle (NP)-supported drug delivery system (DDS), loading efficiency, stable encapsulation, and targeted delivery are considered crucial to achieve a high therapeutic outcome of the resulting system. Conventionally, NPs are functionalized with desired molecules via covalent interactions, which do not only limit the intraparticle space for drug loading but also cause significant loss of the preloaded drug through the multistep chemical reactions. Furthermore, NPs with covalently modified surface are easily surrounded with biomolecules during blood circulation, and their accumulation in a target site becomes considerably hampered. Keeping these issues in mind, we herein summarize the recently reported unconventional strategies to prepare a more powerful DDS with enhanced loading and targeting ability, by installing a noncovalent polymeric gatekeeper or surface-protective biomolecular layer on the NP surface.

Vibrational relaxation in a hydrogen-bonded adenosine monophosphate has been studied in classical dynamics procedures. The initial excitation stored in the phosphate OH vibration is shown to mainly redistribute in the ribose moiety through efficient energy pumping by the phosphoester bond. The efficiency is due to the phosphoester bond couples with neighboring bonds and transports the initial excitation to the ribose in a series of small steps. In the ribose unit, energy travels the carbon–carbon pathway and distributes mainly in the C–O–H side chains. Energy distribution in the phosphate unit is minor, but the extent is significantly higher than the amount distributed in the adenine moiety, which shares only about 10% of the initial excitation due to the inefficient energy pumping of the ribose-to-adenine bond. The numerical procedures are repeated to prevent the leakage of zero-point energies by modifying the kinetic energy for each bond.

Polymers are commonly subjected to ball-mill grinding (BMG) to induce chain scission. Milling and polymer parameters can influence the kinetics of degradation, and various kinetic methods have been employed to study this process. The most common methods include molecular weight models (fitting the decrease in molecular weight with milling time) and refractive index (RI)-based methods (such as the Florea method, which fits the decrease in RI signal with milling time). In this report, we compare the rate constant trends obtained from three different molecular weight models and the Florea method to provide a deeper understanding of how the kinetic method employed in kinetic studies influences the observed reactivity trends. Specifically, each kinetic method was applied to BMG-induced degradation data from polystyrene (PS) and poly(lactide) (PLA) of varying initial molecular weight. Each kinetic method produced different rate constant trends (e.g., initial molecular weight vs. rate constant), which could also be influenced by changing the milling duration. Molecular weight models or the Florea method were better suited with longer or shorter milling durations, respectively. Furthermore, the rate constant trends for PS and PLA were relatively consistent if compared with the same kinetic method. We expect that this work will help guide future BMG-based degradation kinetics studies.

Research studies on antimicrobial peptides (AMPs) that can combat antibiotic-resistant bacteria, an issue with antibiotic usage, are garnering attention. Among the AMP types, cationic AMPs can electrostatically interact with the bacterial membrane and destroy it, resulting in bacterial death. In the presence of metal ions, these cationic AMPs possess beneficial antimicrobial activity. In the present study, through structural change analysis and antimicrobial activity tests, we elucidated whether LPcin-YK5, a bovine-derived AMP analog with enhanced antimicrobial activity, interacts with the divalent ions copper(II) and zinc(II) ions. We observed that the interaction between YK5 and divalent ions contributed to the secondary structure stability of YK5, resulting in a synergistic effect on the antimicrobial activity; this is consistent with the interaction between other cationic AMPs and divalent ions. Interestingly, the secondary structural changes and antimicrobial activity against gram-negative bacteria of YK5 were more notable in the presence of zinc(II) than in that of copper(II). Therefore, the effects of YK5 and divalent ions may have implications for designing antibiotics against antibiotic-resistant microorganisms.

To develop an adsorbent for Li⁺ recovery from seawater and/or spent lithium batteries, a benzo-12-crown-4 ether (B12C4) moiety was immobilized with silica (immobilization yield: 0.70 meq g⁻¹). Compared to pure silica, the resulting adsorbent (FB12C4-SG) had a reduced Brunauer–Emmett–Teller surface area (500 vs. 180 m² g⁻¹) and pore volume (0.75 vs. 0.26 cm³ g⁻¹). The Li⁺ adsorption reached equilibrium at 31 mg g⁻¹ after 2 h (1000 ppm Li⁺ solution). The adsorption behavior was well explained by pseudo-second-order kinetics and the Langmuir adsorption model (maximum adsorption capacity: 33 mg g⁻¹). The material exhibited a Li⁺/Na⁺ adsorption selectivity factor of 4.2 and high chemical stability under acidic regeneration conditions (1.0 N HCl solution).