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ABSTRACT Covert patterns have an extra layer of security protection for anti‐counterfeiting labels as compared with the traditional overt ones. To increase the complexity and security of quick response (QR) codes, it would be valuable to make covert QR codes that will be only scannable after a certain decoding process. In this work, the use of surface‐enhanced Raman spectroscopy (SERS) is explored to fabricate covert QR codes. Through developing new Raman‐active security inks, we can prepare covert QR codes using a convenient inkjet printing method. These QR codes will not be revealed directly. They can only be decoded using a confocal Raman microscope. In addition, multiplex QR codes can be accomplished using multiple Raman probes in printing. Our results showed that the printed QR codes are covert, have strong SERS signals, and can be easily recognized after the SERS decoding. It is anticipated that there is great potential for using such covert and multiplexed SERS‐based QR codes for advanced anti‐counterfeiting applications. 

Group contribution (GC) models are powerful, simple, and popular methods for property prediction. However, the most accessible and computationally efficient GC methods, like the Joback and Reid (JR) GC models, often exhibit severe systematic bias. Furthermore, most GC methods do not have uncertainty estimates associated with their predictions. The present work develops a hybrid method for property prediction that integrates GC models with Gaussian process (GP) regression. Predictions from the JR GC method, along with the molecular weight, are used as input features to the GP models, which learn and correct the systematic biases in the GC predictions, resulting in highly accurate property predictions with reliable uncertainty estimates. The method was applied to six properties: normal boiling temperature (Tb), enthalpy of vaporization at Tb (ΔHvap), normal melting temperature (Tm), critical pressure (Pc), critical molar volume (Vc), and critical temperature (Tc). The CRC Handbook of Chemistry and Physics was used as the primary source of experimental data. The final collected experimental data ranged from 485 molecules for ΔHvap to 5640 for Tm. The proposed GCGP method significantly improved property prediction accuracy compared to the GC-only method. The coefficient of determination (R2) values of the testing set predictions are ≥0.85 for five out of six and ≥0.90 for four out of six properties modeled, and compare favorably with other methods in the literature. Tm was used to demonstrate one way the GCGP method can be tuned for even better predictive accuracy. The GCGP method provides reliable uncertainty estimates and computational efficiency for making new predictions. The GCGP method proved robust to variations in GP model architecture and kernel choice. 

We describe the first comparative data on metal-mediated C-H activation and functionalization reactions at two-carbon-atom legacy refrigerants and DFT analyses on the resulting organometallic products. We reveal that, depending on the degree of fluorination in the refrigerant, C-H activation could lead either to stable M(H)Rf products or to a M(fluoroolefin) complex. The first example of a metal-mediated dehydrofluorination of R-143a is described, resulting from a β-fluoride elimination reaction of a putative [Ir(H)(CH2CF3)] intermediate and the loss of hydrogen fluoride. This work also reports the first examples of metal-mediated activation of R-125 (CF3CF2H) and R-134a (CF3CFH2) where the direct C-H activation products can be observed spectroscopically and, in the case of R-125, structurally characterized. The stability of the [Ir(H)(Rf)] complexes is notable, given that the direct products of C-H activation of non-chelating alkanes at transition metals remains relatively rare. The clean formation of isolable [Ir(H)(Rf)] complexes is expected to facilitate studies of migratory insertion reactions in these species, which bodes well for efforts to repurpose high-global-warming-potential legacy refrigerants. Finally, DFT calculations provide insight into how the degree of fluorination affects C=C bond lengths and energies of propellor-like rotations in the coordinated fluoroolefins, the relative free energies of fluoroolefin binding, and the role of observed CH···F contacts in the calculated structures. 

The combination of machine learning (ML) models with chemistry-related tasks requires the description of molecular structures in a machine-readable way. The nature of these so-called molecular descriptors has a direct and major impact on the performance of ML models and remains an open problem in the field. Structural descriptors like SMILES strings or molecular graphs lack size-independence and can be memory intensive. Machine-learned descriptors can be of low dimensionality and constant size but lack physical significance and human interpretability. Sigma profiles, which are unnormalized histograms of the surface charge distributions of solvated molecules, combine physical significance with low dimensionality and size-independence, making them a suitable candidate for a universal molecular descriptor. However, their widespread adoption in ML applications requires open access to sigma profile generation, which is currently not available. This work details the development of OpenSPGen – an open-source tool for generating sigma profiles. Also presented are studies on the effect of different settings on the efficacy of the generated sigma profiles at predicting thermophysical material properties when used as inputs to a Gaussian process as a simple surrogate ML model. We find that a higher level of theory does not translate to more accurate results. We also provide further recommendations for sigma profile calculation and use in ML models.