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Immunoglobulins are important building blocks in biology and biotechnology. With the emergence of comprehensive deep‐learning approaches, there are enormous opportunities for fast and accurate methods of classification of protein–protein interactions to arise. Herein, widely accessible image classification algorithms for species‐specific typification of a range of different immunoglobulin G (IgG) complexes are repurposed. Droplets of various immunoglobulins mixed with a B‐cell superantigen (SAg) (recombinant staphylococcal Protein A) are deposited onto hydrophobic polymer substrates and the resulting protein stains are imaged using polarized light microscopy. A comprehensive study based on 23 745 images finds that the pretrained convolutional neural network (CNN) InceptionV3 not only successfully categorizes IgGs from four different species but also predicts their binding affinity to Protein A: averaged over 36 binding pairs, the following are observed: 1) an overall accuracy of 81.4%, 2) the highest prediction accuracy for human IgG, the antibody with the highest binding affinity for Protein A, and 3) that the classification accuracy regarding the various IgG/Protein A ratios generally correlates with the binding strength of the protein–protein–complex as determined via circular dichroism spectroscopy. In addition, the CNN pretrained with IgG/Protein A stain images has been challenged with a new set of images using a different superantigen (SAg, Protein G). Despite the use of the unknown SAg, the CNN correctly classifies the images of human IgG and Protein G as indicated by a 94% accuracy over the various molar binding ratios. These findings are noteworthy because they demonstrate that appropriately pretrained CNNs can be used for the prediction of protein–protein interactions beyond the scope of the original training set. Aided by deep‐learning methods, simple stains of mixed protein solutions may serve as accurate predictors of the strength of protein–protein interactions with relevance to protein engineering, self‐aggregation, or protein stability in complex media. [ABSTRACT FROM AUTHOR]

Chemical vapor deposition (CVD) polymerization is a commonly used approach in surface chemistry, providing a substrate‐independent platform for bioactive surface functionalization strategies. This work investigates the Arbuzov reaction of halogenated polymer coatings readily available via CVD polymerization, using poly(4‐chloro‐para‐xylylene) (Parylene C) as a model substance. Postpolymerization modification of these coatings via catalyst‐free and UV‐induced Arbuzov reaction using phosphites results in phosphonate‐functionalized polymers. The combination of infrared reflection‐absorption spectroscopy (IRRAS), X‐ray photoelectron spectroscopy (XPS), and time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) provides detailed insights into the reaction progress. Time‐dependent studies suggest that the non‐polar phosphites penetrate deep into the CVD films and react with the polymer film. In addition, ToF‐SIMS, scanning electron microscopy (SEM), and atomic force microscopy (AFM) confirm spatial control of the reaction, resulting in localized chemical and topographical surface modification, recognizable by changes in interference color, fluorescence, and wettability. Preliminary 3D fluorescence spectroscopy investigations indicate tunable near‐infrared emission of these polymer films. This work is the first step toward generating multifunctional polymer coatings based on chemically modifiable, CVD polymers with potential applications in biomaterials, sensors, or optoelectronics. [ABSTRACT FROM AUTHOR]

Developing scalable and accurate predictive analytical methods for the classification of protein‐DNA binding is critical for advancing our understanding of molecular biology, disease mechanisms, and a wide spectrum of biotechnological and medical applications. It is discovered that histone–DNA interactions can be stratified based on stain patterns created by the deposition of various nucleoprotein solutions onto a substrate. In this study, a deep‐learning neural network is applied to categorize polarized light microscopy images of drying droplet deposits originating from different histone–DNA mixtures. These DNA stain patterns featured high reproducibility across different species and thus enabled comprehensive DNA categorization (100% accuracy) and accurate prediction of their respective binding affinities to histones. Eukaryotic DNA, which has a higher binding affinity to mammalian histones than prokaryotic DNA, is associated with a higher overall prediction accuracy. For a given species, the average prediction accuracy increased with DNA size. To demonstrate generalizability, a pre‐trained CNN is challenged with unknown images that originated from DNA samples of species not included in the training set. The CNN classified these unknown histone‐DNA samples as either strong or medium binders with 84.4% and 96.25% accuracy, respectively. [ABSTRACT FROM AUTHOR]

Metal–organic Frameworks (MOFs), especially as thin films, are increasingly recognized for their potential in device integration, notably in sensors and photo detectors. A critical factor in the performance of many MOF‐based devices is the quality of the MOF interfaces. Achieving MOF thin films with smooth surfaces and low defect densities is essential. Given the extensive parameter space governing MOF thin film deposition, the use of machine learning (ML) methods to optimize deposition conditions is highly beneficial. Combined with robotic fabrication, ML can more effectively explore this space than traditional methods, simultaneously varying multiple parameters to improve optimization efficiency. Importantly, ML can provide deeper insights into the synthesis of MOF thin films, an essential area of research. This study focuses on refining an HKUST‐1 SURMOF (surface‐mounted MOF) to achieve minimal surface roughness and high crystallinity, including a quantitative analysis of the importance of the various synthesis parameters. Using the SyCoFinder ML technique, thin film surface quality is markedly enhanced in just three generations created by a genetic algorithm, covering 30 distinct parameter sets. This method greatly reduces the need for extensive experimentation. Moreover, the results enhance the understanding of the vast synthesis parameter space in HKUST‐1 SURMOF growth and broaden the applications of MOF thin films in electronic and optoelectronic devices. [ABSTRACT FROM AUTHOR]

Many properties of the emerging class of metal‐organic frameworks (MOFs) depend crucially on defect concentrations, as in case of other solids. In order to provide reference systems with nearly perfect structure and low defect density, a procedure to grow MOFs epitaxially on cm‐sized Si(111) single crystals is developed. The crystalline metal‐organic thin films are in high registry with the substrate's crystal lattice, as demonstrated by synchrotron‐based grazing incidence X‐ray diffraction (GI‐XRD) experiments. The corresponding reduction of MOF defect density is shown to have striking effects on the properties of these porous frameworks. The most pronounced difference concerns mass transport. An increase in the diffusion coefficient of guest molecules by one order of magnitude relative to the same MOF materials with normal defect densities is observed. [ABSTRACT FROM AUTHOR]

Exploring innovative strategies for molecular structuring of dynamic materials that combine self‐correcting intrinsic reversibility with the robustness of covalent bonds, has been a long‐standing objective from applications perspective in fields ranging from molecular engineering to nanotechnology and interfacial science. To establish dynamic covalent chemistry approaches combined with interfacial polymerization, herein, a distinct synthetic approach is reported to develop disulfide‐bridged 2D polymeric C3N3S3 triazine thin‐films by interfacial thiol‐disulfide dynamic exchange process crosslinking tritopic planar 1,3,5‐triazine‐2,4,6‐trithiol molecular tectons via intermolecular disulfide formation in the presence of I2 vapors at the air/water interface under redox condition. The resulting centimeter‐scale polymeric thin‐films are covalently cross‐linked, dynamic in nature, featuring tunable thickness (6–200 nm) and significant morphological variations are realized under the influence of varying reaction time, concentration and types of reducing agents. Notably, C3N3S3 polymer thin films exhibit a transflectance of around 99.5% in the range from 430 to 1800 nm, show high refractive indices (1.730–1.488) and optical anisotropy with uniaxial negative birefringence. The C3N3S3 free‐standing polymer thin‐films can be easily transferred to different substrates or possibly into application‐relevant forms for device fabrications, making this useful from materials application perspective. [ABSTRACT FROM AUTHOR]

Understanding the changes that occur in the micro-mechanical properties of semiconductor materials is of utmost importance for the design of new flexible electronic devices, especially to control the properties of newly designed materials. In this work, we present the design, fabrication, and application of a novel tensile-testing device coupled to FTIR measurements that enables in situ atomic investigations of samples under uniaxial tensile load. The device allows for mechanical studies of rectangular samples with dimensions of 30 mm × 10 mm × 0.5 mm. By recording the alternation in dipole moments, the investigation of fracture mechanisms becomes feasible. Our results show that thermally treated SiO 2 on silicon wafers has a higher strain resistance and breaking force than the SiO 2 native oxide. The FTIR spectra of the samples during the unloading step indicate that for the native oxide sample, the fracture happened following the propagation of cracks from the surface into the silicon wafer. On the contrary, for the thermally treated samples, the crack growth starts from the deepest region of the oxide and propagates along the interface due to the change in the interface properties and redistribution of the applied stress. Finally, density functional theory calculations of model surfaces were conducted in order to unravel the differences in optic and electronic properties of the interfaces with and without applied stress., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

As in the case of other semiconducting materials, optical and electronic properties of metal-organic frameworks (MOFs) depend critically on defect densities and defect types. We demonstrate here that, in addition to the influence of imperfections on MOF chemical properties like guest binding energies and catalytic activity, the optical properties of these crystalline molecular solids also crucially depend on deviations from the perfect crystalline structure. By recording UV-vis absorption spectra for MOF thin films of particularly high quality, we demonstrate that low-defect samples of an important MOF, HKUST-1, are virtually colorless. Electronic structure calculations of the excited states by employing complete active space self-consistent field (CASSCF) calculations show that the d-d excitations in defects result in the typical green color of the MOF material synthesized by conventional methods.