Enhancing Efficiency and Stability of Inverted Perovskite Solar Cells through Solution-Processed and Structurally Ordered Fullerene.

The electron transporting layer (ETL) used in high performance inverted perovskite solar cells (PSCs) is typically composed of C60, which requires time-consuming and costly thermal evaporation deposition, posing a significant challenge for large-scale production. To address this challenge, herein, we present a novel design of solution-processible electron transporting material (ETM) by grafting a non-fullerene acceptor fragment onto C60. The synthesized BTPC60 exhibits an exceptional solution processability and well-organized molecular stacking pattern, enabling the formation of uniform and structurally ordered film with high electron mobility. When applied as ETL in inverted PSCs, BTPC60 not only exhibits excellent interfacial contact with the perovskite layer, resulting in enhanced electron extraction and transfer efficiency, but also effectively passivates the interfacial defects to suppress non-radiative recombination. Resultant BTPC60-based inverted PSCs deliver an impressive power conversion efficiency (PCE) of 25.3% and retain almost 90% of the initial values after aging at 85°C for 1500 hours in N2. More encouragingly, the solution-processed BTPC60 ETL demonstrates remarkable film thickness tolerance, and enables a high PCE up to 24.8% with the ETL thickness of 200 nm. Our results highlight BTPC60 as a promising solution-processed fullerene-based ETM, opening an avenue for improving the scalability of efficient and stable inverted PSCs.

Machine learning-based prognostic model for 30-day mortality prediction in Sepsis-3.

BACKGROUND: Sepsis poses a critical threat to hospitalized patients, particularly those in the Intensive Care Unit (ICU). Rapid identification of Sepsis is crucial for improving survival rates. Machine learning techniques offer advantages over traditional methods for predicting outcomes. This study aimed to develop a prognostic model using a Stacking-based Meta-Classifier to predict 30-day mortality risks in Sepsis-3 patients from the MIMIC-III database. METHODS: A cohort of 4,240 Sepsis-3 patients was analyzed, with 783 experiencing 30-day mortality and 3,457 surviving. Fifteen biomarkers were selected using feature ranking methods, including Extreme Gradient Boosting (XGBoost), Random Forest, and Extra Tree, and the Logistic Regression (LR) model was used to assess their individual predictability with a fivefold cross-validation approach for the validation of the prediction. The dataset was balanced using the SMOTE-TOMEK LINK technique, and a stacking-based meta-classifier was used for 30-day mortality prediction. The SHapley Additive explanations analysis was performed to explain the model's prediction. RESULTS: Using the LR classifier, the model achieved an area under the curve or AUC score of 0.99. A nomogram provided clinical insights into the biomarkers' significance. The stacked meta-learner, LR classifier exhibited the best performance with 95.52% accuracy, 95.79% precision, 95.52% recall, 93.65% specificity, and a 95.60% F1-score. CONCLUSIONS: In conjunction with the nomogram, the proposed stacking classifier model effectively predicted 30-day mortality in Sepsis patients. This approach holds promise for early intervention and improved outcomes in treating Sepsis cases.

Parameter optimisation for image acquisition and stacking in carbon dioxide digital subtraction angiography.

The aim of this study was to optimise the vessel angle as well as the stack number from the profiles of carbon dioxide digital subtraction angiography (CO2-DSA) images of a water phantom containing an artificial vessel tilted at different angles which imitate arteries in the body. The artificial vessel was tilted at 0°, 15°, and 30° relative to the horizontal axis with its centre as the pivot point, and CO2-DSA images were acquired at each vessel tilt angle. The maximum opacity method was used to stack up to four images of the next frame one by one. The signal-to-noise ratio (SNR) was determined from the profile curves. The Wilcoxon rank sum test was used to evaluate whether the profile curve and SNR differed depending on the vessel tilt angle or stack number, and a p-value of less than 0.05 was considered statistically significant. Images acquired at 0° had a significantly lower SNR than images acquired at 15° (p = 0.10). When the vessel angle was 30°, the profile curves were significantly improved (p < 0.05) when two or more images were stacked over the original image. Images with a good SNR were acquired at the vessel tilt angle of 15°, and the shape of the profile curve was improved when two or more images were stacked on the original image. This study demonstrates that the quality of images acquired using CO2-DSA can be significantly improved through parameter optimisation for image acquisition and post-processing.

Effects of Fe addition on the mechanical and corrosive properties of biomedical Co-Cr-W-Ni alloys for balloon-expandable stents.

Co-20Cr-15W-10Ni (mass%, CCWN) alloy is extensively used as a platform material for balloon-expandable stents. In this study, the mechanical properties of CCWN alloy are improved following the addition of Fe, and the effects of Fe addition on the mechanical and corrosive properties of the alloy are investigated. As-cast specimens were fabricated by adding pure Fe to a commercially available CCWN alloy (base alloy) such that the resulting alloys contained 4, 6, and 8 mass% Fe. The as-cast specimens were subjected to homogenization heat treatment at 1523 K for 7.2 ks and then hot-forged at 1473 K (as-forged specimens). The as-forged specimens were cold-rolled at a reduction rate of 30% and heat-treated at 1473 K for 300 s (recrystallized specimens). The matrix of the recrystallized base- and Fe-containing alloys consisted of a single γ (face-centered cubic)-phase. The Fe-added alloys revealed precipitates composed of the η-phase (M6X-M12X-type phase, M: metallic element, X: C and/or N). The average grain size of the recrystallized base and Fe-added alloy specimens was approximately 34 µm and the amount of added Fe had no significant effect on the static recrystallization behavior of the resulting alloys. Alloys containing 6 mass% or more Fe showed improvements in strength and ductility compared with the base alloy. When the Fe-added alloys were compared, their strength decreased whereas their ductility increased when the added Fe increased. Because Fe acts as a γ-phase-stabilizing element for Co, Fe addition increases the stacking fault energy of the base alloy, resulting in the formation of the ε (hexagonal close-packed)-phase owing to the suppression of strain-induced martensitic transformation (SIMT), and improvements in ductility. No deterioration in corrosion resistance was observed following the addition of up to 8 mass% Fe to the base alloy. Based on these results, the addition of Fe to CCWN alloy may be considered an effective method to improve its mechanical properties, especially ductility, without impairing its corrosion resistance. The results of this study will be useful for the future development of Ni-free Co-Cr alloys for next-generation, small-diameter stents.

Bis(2-carb-oxy-quinolinium) hexa-chlorido-stan-nate(IV) dihydrate.

In the hydrated title salt, (C10H8NO2)2[SnCl6]·2H2O, the tin(IV) atom is located about a center of inversion. In the crystal structure, the organic cation, the octa-hedral inorganic anion and the water mol-ecule of crystallization inter-act through O-H⋯O, N-H⋯O and O-H⋯Cl hydrogen bonds, supplemented by weak π-π stacking between neighboring cations, and C-Cl⋯π inter-actions.

A Supramolecular-nanocage-based Framework Stabilized by π-π Stacking Interactions with Enhanced Photocatalysis.

π frameworks, defined as a type of porous supramolecular materials weaved from conjugated molecular units by π-π stacking interactions, provide a new direction in photocatalysis. However, such examples are rarely reported. Herein, we report a supramolecular-nanocage-based π framework constructed from a photoactive Cu(I) complex unit. Structurally, 24 Cu(I) complex units stack together through π-π stacking interactions, forming a truncated octahedral nanocage with sodalite topology. The inner diameter of the nanocage is 2.8 nm. By sharing four open faces, each nanocage connects with four equivalent ones, forming a 3D porous π framework (π-2). π-2 shows good thermal and chemical stability, which can adsorb CO2, iodine, and methyl orange molecules. More importantly, π-2 can serve as a photocatalyst for hydrogen evolution reaction. With ultrafine Pt subnanometer particles (0.9±0.1 nm) incorporated into the nanocages as a co-catalyst, the hydrogen evolution rate reaches a record-high value of 517551 µmol/gPt/h in the absence of any additional photosensitizers. The high photocatalytic activity can be ascribed to the ultrafine size of the Pt particles, as well as the fast electron transfer from π-2 to the highly active Pt upon illumination. π-2 represents the unique stable supramolecular-cage-based π framework with excellent photocatalytic activity.

mACPpred 2.0: Stacked Deep Learning for Anticancer Peptide Prediction with Integrated Spatial and Probabilistic Feature Representations.

Anticancer peptides (ACPs), naturally occurring molecules with remarkable potential to target and kill cancer cells. However, identifying ACPs based solely from their primary amino acid sequences remains a major hurdle in immunoinformatics. In the past, several web-based machine learning (ML) tools have been proposed to assist researchers in identifying potential ACPs for further testing. Notably, our meta-approach method, mACPpred, introduced in 2019, has significantly advanced the field of ACP research. Given the exponential growth in the number of characterized ACPs, there is now a pressing need to create an updated version of mACPpred. To develop mACPpred 2.0, we constructed an up-to-date benchmarking dataset by integrating all publicly available ACP datasets. We employed a large-scale of feature descriptors, encompassing both conventional feature descriptors and advanced pre-trained natural language processing (NLP)-based embeddings. We evaluated their ability to discriminate between ACPs and non-ACPs using eleven different classifiers. Subsequently, we employed a stacked deep learning (SDL) approach, incorporating 1D convolutional neural network (1D CNN) blocks and hybrid features. These features included the top seven performing NLP-based features and 90 probabilistic features, allowing us to identify hidden patterns within these diverse features and improve the accuracy of our ACP prediction model. This is the first study to integrate spatial and probabilistic feature representations for predicting ACPs. Rigorous cross-validation and independent tests conclusively demonstrated that mACPpred 2.0 not only surpassed its predecessor (mACPpred) but also outperformed the existing state-of-the-art predictors, highlighting the importance of advanced feature representation capabilities attained through SDL. To facilitate widespread use and accessibility, we have developed a user-friendly for mACPpred 2.0, available at https://balalab-skku.org/mACPpred2/.

Microstructure Evolution of Vacuum Diffusion-Bonded 304 Stainless Steel/20 Carbon Steel Bimetallic Interface after Solution Treatment.

In this study, a 304 stainless steel (304 SS)/20 carbon steel (20 CS) bimetal was prepared by vacuum diffusion bonding, with 20 CS as the substrate and 304 SS as the cladding layer, and the interfacial microstructure and bonding strength before and after solution treatment were studied. The 304 SS and The 20 CS formed a strong metallurgical bond after being held at 1380 °C for 60 min without defects such as unbonded regions. Diffusion of Cr and Ni atoms from the 304 SS to the 20 CS occurred, and the C atoms diffused from the 20 CS to the 304 SS, forming a carburized region. A pearlitic region with an average width of approximately 20 µm was formed on the 20 CS side. After solution treatment, austenitization was formed in the carburized region, which was accompanied by the formation of twin crystals. The interfacial bonding strength of the bimetal was measured to be 485 MPa, which increased to 547 MPa after the solution treatment.

Phase Transition of Cubic Ice to Hexagonal Ice during Growth and Decomposition.

Ice, one of the most enigmatic materials on Earth, exhibits diverse polymorphism, with research mainly focusing on the most commonly observed phases: hexagonal ice (Ih), cubic ice (Ic), and stacking-disordered ice (Isd). While their formation or structural changes are crucial for advancements in cloud science, climate modeling, and cryogenic technology, the molecular mechanisms driving these phenomena remain unexplored. Herein, utilizing cryogenic transmission electron microscopy, we investigate the formation of ice at two different temperatures, demonstrating a size-dependent phase shift from Ic to Isd. Furthermore, a relatively metastable cubic phase in Isd transitions to a hexagonal phase under electron beam radiation. This transition, facilitated by crystal defects, contrasts with perfect crystalline Ic, which maintains its original phase, emphasizing the importance of defects in polymorphic phase transitions. Our findings provide novel insights on phase control during the ice growth processes and polymorphic phase transitions from the cubic-to-hexagonal phases.

Sulfur and Wavy-Stacking Boosted Superior Lithium Storage in 2D Covalent Organic Frameworks.

2D conjugated covalent organic frameworks (c-COFs) provide an attractive foundation as organic electrodes in energy storage devices, but their storage capability is long hindered by limited ion accessibility within densely π-π stacked interlayers. Herein, two kinds of 2D c-COFs based on dioxin and dithiine linkages are reported, which exhibit distinct in-plane configurations-fully planar and undulated layers. X-ray diffraction analysis reveals wavy square-planar networks in dithiine-bridged COF (COF-S), attributed to curved C─S─C bonds in the dithiine linkage, whereas dioxin-bridged COF (COF-O) features densely packed fully planar layers. Theoretical and experimental results elucidate that the undulated stacking within COF-S possesses an expanded layer distance of 3.8 Å and facilitates effective and rapid Li+ storage, yielding a superior specific capacity of 1305 mAh g-1 at 0.5 A g-1, surpassing that of COF-O (1180 mAh g-1 at 0.5 A g-1). COF-S also demonstrates an admirable cycle life with 80.4% capacity retention after 5000 cycles. As determined, self-expanded wavy-stacking geometry, S-enriched dithiine in COF-S enhances the accessibility and redox activity of Li storage, allowing each phthalocyanine core to store 12 Li+ compared to 8 Li+ in COF-O. These findings underscore the elements and stacking modes of 2D c-COFs, enabling tunable layer distance and modulation of accessible ions.

Small molecule π-π stacking promotes efficient photoelectrocatalytic splitting of aqueous hydrogen production from polyaniline.

The efficiency of photoelectrocatalysis is fundamentally dependent on the sufficient absorption of light and efficient utilisation of photogenerated carriers, but is largely limited by the reactivity from the inefficient charge transfer and surface sites of the catalyst. In this study, π-π stacking of polar small molecules on aromatic ring-rich polyaniline (PANI) was carried out to improve its photoelectrocatalytic splitting of water for hydrogen production. Detailed photoelectrochemical experiments and density-functional theory (DFT) calculations show that small molecules of p-aminobenzoic acid (PABA) and PANI have the best π-π stacking (compared to p-toluenesulfonic acid (PTA)), which promotes the separation of carriers on the PANI surface. In addition, the polar effect of the small molecules also improves the reactivity of the PANI surface and also reduces the potential barrier for H2 evolution. The current density of PANI-PABA reached -0.12 mA/cm2 (1.23 V vs. RHE) 2.53 times higher than that of pure PANI in linear voltammetric scanning tests under light. This strategy of introducing polar small molecules into organocatalysts via π-π stacking will provide new ideas for the preparation of efficient organic photoelectrocatalysis.

Highly sensitive two-dimensional profiling of N-linked glycans by hydrophilic interaction liquid chromatography and dual stacking capillary gel electrophoresis.

BACKGROUND: N-Glycosylation is one of the most important post-translational modifications in proteins. As the N-glycan profiles in biological samples are diverse and change according to the pathological condition, various profiling methods have been developed, such as liquid chromatography (LC), capillary electrophoresis (CE), and mass spectrometry. However, conventional analytical methods have limitations in sensitivity and/or resolution, hindering the discovery of minor but specific N-glycans that are important both in the basic glycobiology research and in the medical application as biomarkers. Therefore, a highly sensitive and high-resolution N-glycan profiling method is required. RESULTS: In this study, we developed a novel two-dimensional (2D) separation system, which couples hydrophilic interaction liquid chromatography (HILIC) with capillary gel electrophoresis (CGE) via large-volume dual preconcentration by isotachophoresis and stacking (LDIS). Owing to the efficient preconcentration efficiency of LDIS, limit of detection reached 12 pM (60 amol, S/N = 3) with good calibration curve linearity (R2 > 0.999) in the 2D analysis of maltoheptaose. Finally, 2D profiling of N-glycans obtained from standard glycoproteins and cell lysates were demonstrated. High-resolution 2D profiles were successfully obtained by data alignment using triple internal standards. N-glycans were well distributed on the HILIC/CGE 2D plane based on the glycan size, number of sialic acids, linkage type, and so on. As a result, specific minor glycans were successfully identified in HepG2 and HeLa cell lysates. SIGNIFICANCE AND NOVELTY: In conclusion, the HILIC/CGE 2D analysis method showed sufficient sensitivity and resolution for identifying minor but specific N-glycans from complicated cellular samples, indicating the potential as a next-generation N-glycomics tool. Our novel approach for coupling LC and CE can also dramatically improve the sensitivity in other separation modes, which can be a new standard of 2D bioanalysis applicable not only to glycans, but also to other diverse biomolecules such as metabolites, proteins, and nucleic acids.

Bidirectional Phase Transformations in Multi-Principal Element Alloys: Mechanisms, Physics, and Mechanical Property Implications.

The emergence of multi-principal element alloys (MPEAs) heralds a transformative shift in the design of high-performance alloys. Their ingrained chemical complexities endow them with exceptional mechanical and functional properties, along with unparalleled microscopic plastic mechanisms, sparking widespread research interest within and beyond the metallurgy community. In this overview, a unique yet prevalent mechanistic process in the renowned FeMnCoCrNi-based MPEAs is focused on: the dynamic bidirectional phase transformation involving the forward transformation from a face-centered-cubic (FCC) matrix into a hexagonal-close-packed (HCP) phase and the reverse HCP-to-FCC transformation. The light is shed on the fundamental physical mechanisms and atomistic pathways of this intriguing dual-phase transformation. The paramount material parameter of intrinsic negative stacking fault energy in MPEAs and the crucial external factors c, furnishing thermodynamic, and kinetic impetus to trigger bidirectional transformation-induced plasticity (B-TRIP) mechanisms， are thorougly devled into. Furthermore, the profound significance of the distinct B-TRIP behavior in shaping mechanical properties and creating specialized microstructures c to harness superior material characteristics is underscored. Additionally, critical insights are offered into key challenges and future striving directions for comprehensively advancing the B-TRIP mechanism and the mechanistic design of next-generation high-performing MPEAs.

Self-Anchored Van-Der-Waals Stacking Growth of Transition-Metal Dichalcogenide Nanoplates.

Transition-metal dichalcogenide (TMDs) nanoplates exhibit unique properties different from their monolayer counterparts. Controllable nucleation and growth are prerequisite and highly desirable for their practical applications. Here, a self-anchored van-der-Waals stacking growth method is developed, by which the substrate pit induced by precursor etching anchors the source material, impedes the lateral spreading of source droplets and facilitates the in situ stacking growth of high-quality TMD nanoplates with a thickness of tens to hundreds of nanometers at well-defined locations. As such, an array of TMD nanoplates with controlled lateral dimensions are produced and applied in arrayed photodetectors. This study solves the problem of controllable preparation of TMD nanoplates, holding promise for applications in electronics and optoelectronics.

A stacking ensemble model for predicting the occurrence of carotid atherosclerosis.

Background: Carotid atherosclerosis (CAS) is a significant risk factor for cardio-cerebrovascular events. The objective of this study is to employ stacking ensemble machine learning techniques to enhance the prediction of CAS occurrence, incorporating a wide range of predictors, including endocrine-related markers. Methods: Based on data from a routine health check-up cohort, five individual prediction models for CAS were established based on logistic regression (LR), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost) and gradient boosting decision tree (GBDT) methods. Then, a stacking ensemble algorithm was used to integrate the base models to improve the prediction ability and address overfitting problems. Finally, the SHAP value method was applied for an in-depth analysis of variable importance at both the overall and individual levels, with a focus on elucidating the impact of endocrine-related variables. Results: A total of 441 of the 1669 subjects in the cohort were finally diagnosed with CAS. Seventeen variables were selected as predictors. The ensemble model outperformed the individual models, with AUCs of 0.893 in the testing set and 0.861 in the validation set. The ensemble model has the optimal accuracy, precision, recall and F1 score in the validation set, with considerable performance in the testing set. Carotid stenosis and age emerged as the most significant predictors, alongside notable contributions from endocrine-related factors. Conclusion: The ensemble model shows enhanced accuracy and generalizability in predicting CAS risk, underscoring its utility in identifying individuals at high risk. This approach integrates a comprehensive analysis of predictors, including endocrine markers, affirming the critical role of endocrine dysfunctions in CAS development. It represents a promising tool in identifying high-risk individuals for the prevention of CAS and cardio-cerebrovascular diseases.

Long-Range π-π Stacking Brings High Electron Delocalization for Enhanced Photocatalytic Activity in Hydrogen-Bonded Organic Framework.

Unlike many studies that regulate transport and separation behaviour of photogenerated charge carriers through controlling the chemical composite, our work demonstrates this goal can be achieved through simply tuning the molecular π-π packing from short-range to long-range within hydrogen-bonded organic frameworks (HOFs) without altering the building blocks or network topology. Further investigations reveal that the long-range π-π stacking significantly promotes electron delocalization and enhances electron density, thereby effectively suppressing electron-hole recombination and augmenting the charge transfer rate. Simultaneously, acting as a porous substrate, it boosts electron density of Pd nanoparticle loaded on its surfaces, resulting in remarkable CO2 photoreduction catalytic activity (CO generation rate: 48.1 µmol/g/h) without the need for hole scavengers. Our study provide insight into regulating the charge carrier behaviours in molecular assemblies based on hydrogen bonds, offering a new clue for efficient photocatalyst design.

A feasibility study of tumor motion monitoring for SBRT of lung cancer based on 3D point cloud detection and stacking ensemble learning.

PURPOSE: To construct a tumor motion monitoring model for stereotactic body radiation therapy (SBRT) of lung cancer from a feasibility perspective. METHODS: A total of 32 treatment plans for 22 patients were collected, whose planning CT and the centroid position of the planning target volume (PTV) were used as the reference. Images of different respiratory phases in 4DCT were acquired to redefine the targets and obtain the floating PTV centroid positions. In accordance with the planning CT and CBCT registration parameters, data augmentation was accomplished, yielding 2130 experimental recordings for analysis. We employed a stacking multi-learning ensemble approach to fit the 3D point cloud variations of body surface and the change of target position to construct the tumor motion monitoring model, and the prediction accuracy was assess using root mean squared error (RMSE) and R-Square (R2). RESULTS: The prediction displacement of the stacking ensemble model shows a high degree of agreement with the reference value in each direction. In the first layer of model, the X direction (RMSE =0.019 ∼ 0.145mm, R2 =0.9793∼0.9996) and the Z direction (RMSE = 0.051 ∼ 0.168 mm, R2 = 0.9736∼0.9976) show the best results, while the Y direction ranked behind (RMSE = 0.088 ∼ 0.224 mm, R2 = 0.9553∼ 0.9933). The second layer model summarizes the advantages of unit models of first layer, and RMSE of 0.015 mm, 0.083 mm, 0.041 mm, and R2 of 0.9998, 0.9931, 0.9984 respectively for X, Y, Z were obtained. CONCLUSIONS: The tumor motion monitoring method for SBRT of lung cancer has potential application of non-ionization, non-invasive, markerless, and real-time.

Radio Signal Modulation Recognition Method Based on Hybrid Feature and Ensemble Learning: For Radar and Jamming Signals.

The detection performance of radar is significantly impaired by active jamming and mutual interference from other radars. This paper proposes a radio signal modulation recognition method to accurately recognize these signals, which helps in the jamming cancellation decisions. Based on the ensemble learning stacking algorithm improved by meta-feature enhancement, the proposed method adopts random forests, K-nearest neighbors, and Gaussian naive Bayes as the base-learners, with logistic regression serving as the meta-learner. It takes the multi-domain features of signals as input, which include time-domain features including fuzzy entropy, slope entropy, and Hjorth parameters; frequency-domain features, including spectral entropy; and fractal-domain features, including fractal dimension. The simulation experiment, including seven common signal types of radar and active jamming, was performed for the effectiveness validation and performance evaluation. Results proved the proposed method's performance superiority to other classification methods, as well as its ability to meet the requirements of low signal-to-noise ratio and few-shot learning.

Revisiting the Most Stable Structures of the Benzene Dimer.

The benzene dimer (BD) is an archetypal model of π∙∙∙π and C-H∙∙∙π noncovalent interactions as they occur in its cofacial and perpendicular arrangements, respectively. The enthalpic stabilization of the related BD structures has been debated for a long time and is revisited here. The revisit is based on results of computations that apply the coupled-cluster theory with singles, doubles and perturbative triples [CCSD(T)] together with large basis sets and extrapolate results to the complete basis set (CBS) limit in order to accurately characterize the three most important stationary points of the intermolecular interaction energy (ΔE) surface of the BD, which correspond to the tilted T-shaped (TT), fully symmetric T-shaped (FT) and slipped-parallel (SP) structures. In the optimal geometries obtained by searching extensive sets of the CCSD(T)/CBS ΔE data of the TT, FT and SP arrangements, the resulting ΔE values were -11.84, -11.34 and -11.21 kJ/mol, respectively. The intrinsic strength of the intermolecular bonding in these configurations was evaluated by analyzing the distance dependence of the CCSD(T)/CBS ΔE data over wide ranges of intermonomer separations. In this way, regions of the relative distances that favor BD structures with either π∙∙∙π or C-H∙∙∙π interactions were found and discussed in a broader context.

Development of a dispersive solid phase microextraction method based on the application of MgAl, NiAl, and CoAl-layered double hydroxides for the efficient removal of α- and ß-naphthol isomers from water samples by capillary electrophoresis.

The present work describes a quick, simple, and efficient method based on the use of layered double hydroxides (LDH) coupled to dispersive solid phase micro-extraction (DSPME) to remove α-naphthol (α-NAP) and ß-naphthol (ß-NAP) isomers from water samples. Three different LDHs (MgAl-LDH, NiAl-LDH, and CoAl-LDH) were used to study how the interlayer anion and molar ratio affected the removal performance. The critical factors in the DSPME procedure (pH, LDH amount, contact time) were optimized by the univariate method under the optimal conditions: pH, 4-8; LDH amount, 5 mg; and contact time, 2.5 min. The method can be successfully applied in real sample waters, removing NAP isomers even in ultra-trace concentrations. The large volume sample stacking (LVSS-CE) technique provides limits of detections (LODs) of 5.52 µg/L and 6.36 µg/L for α-naphthol and ß-naphthol, respectively. The methodology's precision was evaluated on intra- and inter-day repeatability, with %RSD less than 10% in all cases. The MgAl/Cl--LDH selectivity was tested in the presence of phenol and bisphenol A, with a removal rate of >92.80%. The elution tests suggest that the LDH MgAl/Cl--LDH could be suitable for pre-concentration of α-naphthol and ß-naphthol in future works.