Expert System for Fourier Transform Infrared Spectra Recognition Based on a Convolutional Neural Network With Multiclass Classification

Fourier transform infrared spectroscopy (FT-IR) is a widely used spectroscopic method for routine analysis of substances and compounds. Spectral interpretation of spectra is a labor-intensive process that provides important information about functional groups or bonds present in compounds and complex substances. In this paper, based on deep learning methods of convolutional neural networks, models were developed to determine the presence of 17 classes of functional groups or 72 classes of coupling oscillations in the FT-IR spectra. Using web scanning, the spectra of 14 361 FT-IR spectra of organic molecules were obtained. Several different variants of model architectures with different sizes of feature maps have been tested. Based on the Shapley additive explanations (SHAP) and gradient-weighted class activation mapping (GradCAM) methods, visualization tools have been developed for visualizing and highlighting the areas of absorption bands manifestation for corresponding functional groups or bonds in the spectrum. To determine 17 and 72 classes, the F1-weighted metric, which is the harmonic mean of the class' precision and class' recall weighted by class' fraction, reached 93 and 88%, respectively, when using data on the position of absorption maxima in the spectrum as an additional source layer. The resulting model can be used to facilitate the routine analysis of spectra for all areas such as organic chemistry, materials science, and biology, as well as to facilitate the preparation of the obtained experimental data for publication.

Various Structural Design Modifications: para-Substituted Diphenylphosphinopyridine Bridged Cu(I) Complexes in Organic Light-Emitting Diodes

The well-known system of dinuclear Cu(I) complexes bridged by 2-(diphenylphosphino)pyridine (PyrPhos) derivatives Cu₂X₂L₃ and Cu₂X₂LP₂ (L = bridging ligand, P = ancillary ligand) goes along with endless variation options for tunability. In this work, the influence of substituents and modifications on the phosphine moiety of the NP-bridging ligand was investigated. In previous studies, the location of the lowest unoccupied molecular orbital (LUMO) of the copper complexes of the PyrPhos family was found to be located on the NP-bridging ligand and enabled color tuning in the whole visible spectrum. A multitude of dinuclear Cu(I) complexes based on the triple methylated 2-(bis(4-methylphenyl)phosphino)-4-methylpyridine (Cu-1b-H, Cu-1b-MeO, and Cu-1b-F) up to complexes bearing 2-(bis(4-fluorophenyl)phosphino)pyridine (Cu-6a-H) with electron-withdrawing fluorine atoms over many other variations on the NP-bridging ligands were synthesized. Almost all copper complexes were confirmed via single crystal X-ray diffraction analysis. Besides theoretical TDDFT-studies of the electronic properties and photophysical measurements, the majority of the phosphino-modified Cu(I) complexes was tested in solution-processed organic light-emitting diodes (OLEDs) with different heterostructure variations. The best results of the OLED devices were obtained with copper emitter Cu-1b-H in a stack architecture of ITO/PEDOT-PSS (50 nm)/poly-TPD (15 nm)/20 wt % Cu(I) emitter:CBP:TcTA(7:3) (45 nm)/TPBi (30 nm)/LiF(1 nm)/Al (>100 nm) with a high brightness of 5900 Cd/m² and a good current efficiency of 3.79 Cd/A.