Stacking Interactions: A Supramolecular Approach to Upgrade Weak Halogen Bond Donors.

The co-crystallization of tetracyanobenzene (TCB) with haloarenes ArX provided six new co-crystals TCB ⋅ ArX (ArX=PhCl, PhBr, 4-MeC6 H4 Cl, 4-MeC6 H4 Br, 4-MeOC6 H4 Cl, 1,2-Br2 C6 H4 ) which were studied by X-ray diffraction. In these systems, the strong collective effect of π⋅⋅⋅π stacking interactions and lone pair-(X)⋅⋅⋅π-hole-(C) bondings between TCB and ArX promote the strength of X⋅⋅⋅Ncyano halogen bonding (HaB). Theoretical studies showed that the stacking interactions affect the σ-hole depth of the haloarenes, thus significantly boosting their ability to function as HaB donors. According to the molecular electrostatic potential calculations, the σ- hole-(Cl) value (1.5 kcal/mol) in the haloarene 4-MeOC6 H4 Cl (featuring an electron-rich arene moiety and exhibiting very poor σ-hole-(Cl) ability) increases significantly in the stacked trimer (TCB)2 ⋅ 4-MeOC6 H4 Cl (12.5 kcal/mol). Theoretical DFT calculations demonstrate the dramatic increase of X⋅⋅⋅Ncyano HaB strength for stacked trimers in comparison with parent unstacked haloarenes.

Revisiting the Self-Assembly of Highly Aromatic Phenylalanine Homopeptides.

Diphenylalanine peptide (FF), which self-assembles into rigid tubular nanostructures, is a very short core recognition motif in Alzheimer's disease ß-amyloid (Aß) polypeptide. Moreover, the ability of the phenylalanine (F or Phe)-homopeptides to self-assemble into ordered nanostructures has been proved. Within this context it was shown that the assembly preferences of this family of compounds is altered by capping both the N- and C-termini using highly aromatic fluorenyl groups (i.e., fluorenyl-9-methoxycarbonyl and 9-fluorenylmethyl ester, named Fmoc and OFm, respectively). In this article the work performed in the field of the effect of the structure and incubation conditions on the morphology and polymorphism of short (from two to four amino acid residues) Phe-homopeptides is reviewed and accompanied by introducing some new results for completing the comparison. Special attention has been paid to the influence of solvent: co-solvent mixture used to solubilize the peptide, the peptide concentration and, in some cases, the temperature. More specifically, uncapped (FF, FFF, and FFFF), N-capped with Fmoc (Fmoc-FF, Fmoc-FFF, and Fmoc-FFFF), C-capped with OFm (FF-OFm), and doubly capped (Fmoc-FF-OFm, Fmoc-FFF-OFm, and Fmoc-FFFF-OFm) Phe-homopeptides have been re-measured. Although many of the experienced assembly conditions have been only revisited as they were previously reported, other experimental conditions have been examined by the first time in this work. In any case, pooling the effect of highly aromatic blocking groups in a single study, using a wide variety of experimental conditions, allows a perspective of how the disappearance of head-to-tail electrostatic interactions and the gradual increase in the amount of π-π stacking interactions, affects the morphology of the assemblies. Future technological applications of Phe-homopeptides can be envisaged by choosing the most appropriate self-assemble structure, defining not only the length of the peptide but also the amount and the position of fluorenyl capping groups.

Improving diabetes disease patients classification using stacking ensemble method with PIMA and local healthcare data.

Diabetes mellitus, a chronic metabolic disorder, continues to be a major public health issue around the world. It is estimated that one in every two diabetics is undiagnosed. Early diagnosis and management of diabetes can also prevent or delay the onset of complications. With the help of a variety of machine learning and deep learning models, stacking algorithms, and other techniques, our study's goal is to detect diseases early. In this study, we propose two stacking-based models for diabetes disease classification using a combination of the PIMA Indian diabetes dataset, simulated data, and additional data collected from a local healthcare facility. We use both the classical and deep neural network stacking ensemble methods to combine the predictions of multiple classification models and improve classification accuracy and robustness. In the evaluation protocol, we used both the train-test and cross-validation (CV) techniques to validate our proposed model. The highest accuracy is obtained by stacking ensemble with three NN architectures, resulting in an accuracy of 95.50 %, precision of 94 %, recall of 97 %, and f1-score of 96 % using 5-fold CV on simulation study. The stacked accuracy obtained from ML algorithms for the Pima Indian Diabetes dataset is 75.03 % using the train-test split protocol, while the accuracy obtained from the CV protocol is 77.10 % on the stacked model. The range of performance scores that outperformed the CV protocol 2.23 %-12 %. Our proposed method achieves a high accuracy range from 92 % to 95 %, precision, recall, and F1-score ranges from 88 % to 96 % using classical and deep neural network (NN)-based stacking method on the primary dataset. The proposed dataset and ensemble method could be useful in the early detection and treatment of diabetes, as well as in the advancement of machine learning and data analysis techniques in the healthcare industry.

Process-related Changes in Polyetherimide Joined by Friction-based Injection Clinching Joining (F-ICJ).

This work presents a comprehensive study on the effects of the Friction-based Injection Clinching Joining (F-ICJ) process on the microstructure and local properties of the stake head. The manuscript evaluates the consequences on the quasi-static mechanical performance of hybrid joints of amorphous polyetherimide (PEI) with aluminium AA6082. Through an overlay of microhardness map on a cross-polarized transmitted-light optical microscopy (CP-TLOM) image, two lower-strength microstructural zones in the PEI stake head were observed: a plastically-deformed zone (PDZ) and a thermo-mechanically-affected zone (PTMAZ). When compared to the base material, PDZ and PTMAZ have a reduction of 12%-16% and 8%-12%, respectively, in local mechanical properties. The reduced local strength was associated with distinct volumes of loosely packed PEI chains with unsteady chain conformation and thus larger free volume in the affected regions. The mechanical strength reduction is reversible through physical aging by thermal annealing the joints, which additionally shows that process-induced thermomechanical degradation of PEI by chain scission, as evidenced by size exclusion chromatography (SEC) analysis, does not appear to affect local mechanical strength. An evaluation of typical loading regimes of staked joints in lap shear (average ultimate force of 1419 ± 43 N) and cross tensile (average ultimate force of 430 ± 44 N) testing indicates that the process-induced changes of PEI do not compromise the global mechanical performance of such a structure. These findings provide a better understanding of the relationships between processing, microstructure, and properties for further F-ICJ process optimization.

Characterization of 4H- and 6H-Like Stacking Faults in Cross Section of 3C-SiC Epitaxial Layer by Room-Temperature µ-Photoluminescence and µ-Raman Analysis.

We report a comprehensive investigation on stacking faults (SFs) in the 3C-SiC cross-section epilayer. 3C-SiC growth was performed in a horizontal hot-wall chemical vapour deposition (CVD) reactor. After the growth (85 microns thick), the silicon substrate was completely melted inside the CVD chamber, obtaining free-standing 4 inch wafers. A structural characterization and distribution of SFs was performed by µ-Raman spectroscopy and room-temperature µ-photoluminescence. Two kinds of SFs, 4H-like and 6H-like, were identified near the removed silicon interface. Each kind of SFs shows a characteristic photoluminescence emission of the 4H-SiC and 6H-SiC located at 393 and 425 nm, respectively. 4H-like and 6H-like SFs show different distribution along film thickness. The reported results were discussed in relation with the experimental data and theoretical models present in the literature.