RMG Database for Chemical Property Prediction.

The Reaction Mechanism Generator (RMG) database for chemical property prediction is presented. The RMG database consists of curated datasets and estimators for accurately predicting the parameters necessary for constructing a wide variety of chemical kinetic mechanisms. These datasets and estimators are mostly published and enable prediction of thermodynamics, kinetics, solvation effects, and transport properties. For thermochemistry prediction, the RMG database contains 45 libraries of thermochemical parameters with a combination of 4564 entries and a group additivity scheme with 9 types of corrections including radical, polycyclic, and surface absorption corrections with 1580 total curated groups and parameters for a graph convolutional neural network trained using transfer learning from a set of >130 000 DFT calculations to 10 000 high-quality values. Correction schemes for solvent-solute effects, important for thermochemistry in the liquid phase, are available. They include tabulated values for 195 pure solvents and 152 common solutes and a group additivity scheme for predicting the properties of arbitrary solutes. For kinetics estimation, the database contains 92 libraries of kinetic parameters containing a combined 21 000 reactions and contains rate rule schemes for 87 reaction classes trained on 8655 curated training reactions. Additional libraries and estimators are available for transport properties. All of this information is easily accessible through the graphical user interface at https://rmg.mit.edu. Bulk or on-the-fly use can be facilitated by interfacing directly with the RMG Python package which can be installed from Anaconda. The RMG database provides kineticists with easy access to estimates of the many parameters they need to model and analyze kinetic systems. This helps to speed up and facilitate kinetic analysis by enabling easy hypothesis testing on pathways, by providing parameters for model construction, and by providing checks on kinetic parameters from other sources.

Development of a surface plasmon resonance and nanomechanical biosensing hybrid platform for multiparametric reading.

We have developed a hybrid platform that combines two well-known biosensing technologies based on quite different transducer principles: surface plasmon resonance and nanomechanical sensing. The new system allows the simultaneous and real-time detection of two independent parameters, refractive index change (Δn), and surface stress change (Δσ) when a biomolecular interaction takes place. Both parameters have a direct relation with the mass coverage of the sensor surface. The core of the platform is a common fluid cell, where the solution arrives to both sensor areas at the same time and under the same conditions (temperature, velocity, diffusion, etc.).The main objective of this integration is to achieve a better understanding of the physical behaviour of the transducers during sensing, increasing the information obtained in real time in one single experiment. The potential of the hybrid platform is demonstrated by the detection of DNA hybridization.

Suitable combination of noble/ferromagnetic metal multilayers for enhanced magneto-plasmonic biosensing.

We present a theoretical and experimental study on the biosensing sensitivity of Au/Co/Au multilayers as transducers of the magneto-optic surface-plasmon-resonance (MOSPR) sensor. We demonstrate that the sensing response of these magneto-plasmonic (MP) transducers is a trade-off between the optical absorption and the magneto-optical activity, observing that the MP multilayer with larger MO effect does not provide the best sensing response. We show that it is possible to design highly-sensitive MP transducers able to largely surpass the limit of detection of the conventional surface-plasmon-resonance (SPR) sensor. This was proved comparing the biosensing performance of both sensors for the label-free detection of short DNA chains hybridization. For this purpose, we used and tested a novel label-free biofunctionalization protocol based on polyelectrolytes, which increases the resistance of MP transducers in aqueous environments.