Water-Soluble Naked Gold Nanoclusters Are Not Luminescent.

Here, the synthesis of water-dispersible naked gold nanoclusters (AuNCnaked ) is reported by a simple reduction of HAuCl4 with NaOH at room temperature, and it is shown that they are non-luminescent. They are then easily passivated with different thiols and adenosine monophosphate, leading to luminescent NCs. This is an important finding because the photoluminescence of the passivated NCs can now be clearly attributed to the ligand-AuNC surface interaction. These results are also highly relevant from the point of view of the preparation of luminescent NCs from the same NC batch. This strategy can be valuable for the preparation of a broad range of nano(bio)composites.

The effect of the chemical composition on the sensory characterization of Ecuadorian coffee.

The study aims to test if Ecuadorean coffee's symbolic and material contents agree with the instrumental analysis and grading protocols. We studied the relationship between the chemical composition and the organoleptic characteristics of eight non-specialty and six specialty coffee samples. Firstly, the study addresses the grading following the Specialty Coffee American Association (SCAA) method. The second stage focuses on the qualitative composition of the coffee brews employing GC-MS and caffeine concentrations using HPLC. Then, we employed statistical tools such as Cohen's concordance coefficients, dissimilarity dendrograms, and linear correlations between the chemical compounds in the beverage and the attributes' scores. The grading panel consisted of 6 semi-trained-testers who would assess if the primary cultural capital can provide a criterion to identify specialty coffee. The variety of compounds allowed the evaluators to distinguish between commercial and specialty coffees. However, the composition analysis identified molecules that would imply greater gradation in the tasting, a prevision that was not reflected in the results. Finally, we confirmed that basic training could create cultural capital to distinguish non-specialties from specialty coffees through their chemistry and organoleptic attributes.

Application of Machine Learning Techniques to an Agent-Based Model of Pantoea.

Agent-based modeling (ABM) is a powerful simulation technique which describes a complex dynamic system based on its interacting constituent entities. While the flexibility of ABM enables broad application, the complexity of real-world models demands intensive computing resources and computational time; however, a metamodel may be constructed to gain insight at less computational expense. Here, we developed a model in NetLogo to describe the growth of a microbial population consisting of Pantoea. We applied 13 parameters that defined the model and actively changed seven of the parameters to modulate the evolution of the population curve in response to these changes. We efficiently performed more than 3,000 simulations using a Python wrapper, NL4Py. Upon evaluation of the correlation between the active parameters and outputs by random forest regression, we found that the parameters which define the depth of medium and glucose concentration affect the population curves significantly. Subsequently, we constructed a metamodel, a dense neural network, to predict the simulation outputs from the active parameters and found that it achieves high prediction accuracy, reaching an R 2 coefficient of determination value up to 0.92. Our approach of using a combination of ABM with random forest regression and neural network reduces the number of required ABM simulations. The simplified and refined metamodels may provide insights into the complex dynamic system before their transition to more sophisticated models that run on high-performance computing systems. The ultimate goal is to build a bridge between simulation and experiment, allowing model validation by comparing the simulated data to experimental data in microbiology.

Reversible pH-induced fluorescence colour change of gold nanoclusters based on pH-regulated surface interactions.

To prepare water-dispersible, biocompatible, ratiometric pH nanosensors is challenging. We report here for the first time that the emission colour of NAD+-capped AuNCs responds to the mono-/bidentate anchoring of the phosphoric groups of the ligand. The AuNCs exhibit a high luminescence (21% quantum yield) and an outstanding performance as fluorescent ratiometric pH sensors over a broad pH range.