Multi-Variable Multi-Metric Optimization of Self-Assembled Photocatalytic CO2 Reduction Performance Using Machine Learning Algorithms.

The sunlight-driven reduction of CO2 into fuels and platform chemicals is a promising approach to enable a circular economy. However, established optimization approaches are poorly suited to multivariable multimetric photocatalytic systems because they aim to optimize one performance metric while sacrificing the others and thereby limit overall system performance. Herein, we address this multimetric challenge by defining a metric for holistic system performance that takes multiple figures of merit into account, and employ a machine learning algorithm to efficiently guide our experiments through the large parameter matrix to make holistic optimization accessible for human experimentalists. As a test platform, we employ a five-component system that self-assembles into photocatalytic micelles for CO2-to-CO reduction, which we experimentally optimized to simultaneously improve yield, quantum yield, turnover number, and frequency while maintaining high selectivity. Leveraging the data set with machine learning algorithms allows quantification of each parameter's effect on overall system performance. The buffer concentration is unexpectedly revealed as the dominating parameter for optimal photocatalytic activity, and is nearly four times more important than the catalyst concentration. The expanded use and standardization of this methodology to define and optimize holistic performance will accelerate progress in different areas of catalysis by providing unprecedented insights into performance bottlenecks, enhancing comparability, and taking results beyond comparison of subjective figures of merit.

Realizing Symmetry-Breaking Architectures in Soap Films.

We show here that soap films-typically expected to host symmetric molecular arrangements-can be constructed with differing opposite surfaces, breaking their symmetry, and making them reminiscent of functional biological motifs found in nature. Using fluorescent molecular probes as dopants on different sides of the film, resonance energy transfer could be employed to confirm the lack of symmetry, which was found to persist on timescales of several minutes. Further, a theoretical analysis of the main transport phenomena involved yielded good agreement with the experimental observations.

Emergence of Electric Fields at the Water-C12E6 Surfactant Interface.

We study the properties of the interface of water and the surfactant hexaethylene glycol monododecyl ether (C12E6) with a combination of heterodyne-detected vibrational sum frequency generation (HD-VSFG), Kelvin-probe measurements, and molecular dynamics (MD) simulations. We observe that the addition of the hydrogen-bonding surfactant C12E6, close to the critical micelle concentration (CMC), induces a drastic enhancement in the hydrogen bond strength of the water molecules close to the interface, as well as a flip in their net orientation. The mutual orientation of the water and C12E6 molecules leads to the emergence of a broad (∼3 nm) interface with a large electric field of ∼1 V/nm, as evidenced by the Kelvin-probe measurements and MD simulations. Our findings may open the door for the design of novel electric-field-tuned catalytic and light-harvesting systems anchored at the water-surfactant-air interface.

Intrinsic map dynamics exploration for uncharted effective free-energy landscapes.

We describe and implement a computer-assisted approach for accelerating the exploration of uncharted effective free-energy surfaces (FESs). More generally, the aim is the extraction of coarse-grained, macroscopic information from stochastic or atomistic simulations, such as molecular dynamics (MD). The approach functionally links the MD simulator with nonlinear manifold learning techniques. The added value comes from biasing the simulator toward unexplored phase-space regions by exploiting the smoothness of the gradually revealed intrinsic low-dimensional geometry of the FES.

Mesoscopic Moment Equations for Heat Conduction: Characteristic Features and Slow-Fast Mode Decomposition.

In this work, we derive different systems of mesoscopic moment equations for the heat-conduction problem and analyze the basic features that they must hold. We discuss two- and three-equation systems, showing that the resulting mesoscopic equation from two-equation systems is of the telegraphist's type and complies with the Cattaneo equation in the Extended Irreversible Thermodynamics Framework. The solution of the proposed systems is analyzed, and it is shown that it accounts for two modes: a slow diffusive mode, and a fast advective mode. This latter additional mode makes them suitable for heat transfer phenomena on fast time-scales, such as high-frequency pulses and heat transfer in small-scale devices. We finally show that, if proper initial conditions are provided, the advective mode disappears, and the solution of the system tends asymptotically to the transient solution of the classical parabolic heat-conduction equation.

Thermal transport across nanoparticle-fluid interfaces: the interplay of interfacial curvature and nanoparticle-fluid interactions.

We investigate the general dependence of the thermal transport across nanoparticle-fluid interfaces using molecular dynamics computations. We show that the thermal conductance depends strongly both on the wetting characteristics of the nanoparticle-fluid interface and on the nanoparticle size. Strong nanoparticle-fluid interactions, leading to full wetting states in the host fluid, result in high thermal conductances and efficient interfacial transport of heat. Weak interactions result in partial drying or full drying states, and low thermal conductances. The variation of the thermal conductance with particle size is found to depend on the fluid-nanoparticle interactions. Strong interactions coupled with large interfacial curvatures lead to optimum interfacial heat transport. This complex dependence can be modelled using an equation that includes the interfacial curvature as a parameter. In this way, we rationalise the existing experimental and computer simulation results and show that the thermal transport across nanoscale interfaces is determined by the correlations of both interfacial curvature and nanoparticle-fluid interactions.

Hierarchically-Structured Magnetic Nanoconstructs with Enhanced Relaxivity and Cooperative Tumor Accumulation.

Iron oxide nanoparticles are formidable multifunctional systems capable of contrast enhancement in magnetic resonance imaging; guidance under remote fields; heat generation; and biodegradation. Yet, this potential is underutilized in that each function manifests at different nanoparticle sizes. Here, sub-micrometer discoidal magnetic nanoconstructs are realized by confining 5 nm ultra-small super-paramagnetic iron oxide nanoparticles (USPIOs) within two different mesoporous structures, made out of silicon and polymers. These nanoconstructs exhibit transversal relaxivities up to ~10 times (r2 ~ 835 (mM·s)-1) higher than conventional USPIOs and, under external magnetic fields, collectively cooperate to amplify tumor accumulation. The boost in r2 relaxivity arises from the formation of mesoscopic USPIO clusters within the porous matrix, inducing a local reduction in water molecule mobility as demonstrated via molecular dynamics simulations. The cooperative accumulation under static magnetic field derives from the large amount of iron that can be loaded per nanoconstuct (up to ~ 65 fg) and the consequent generation of significant inter-particle magnetic dipole interactions. In tumor bearing mice, the silicon-based nanoconstructs provide MRI contrast enhancement at much smaller doses of iron (~ 0.5 mg of Fe/kg animal) as compared to current practice.

The global relaxation redistribution method for reduction of combustion kinetics.

An algorithm based on the Relaxation Redistribution Method (RRM) is proposed for constructing the Slow Invariant Manifold (SIM) of a chosen dimension to cover a large fraction of the admissible composition space that includes the equilibrium and initial states. The manifold boundaries are determined with the help of the Rate Controlled Constrained Equilibrium method, which also provides the initial guess for the SIM. The latter is iteratively refined until convergence and the converged manifold is tabulated. A criterion based on the departure from invariance is proposed to find the region over which the reduced description is valid. The global realization of the RRM algorithm is applied to constant pressure auto-ignition and adiabatic premixed laminar flames of hydrogen-air mixtures.

Enhancing ReaxFF for molecular dynamics simulations of lithium-ion batteries: an interactive reparameterization protocol.

Lithium-ion batteries (LIBs) have become an essential technology for the green economy transition, as they are widely used in portable electronics, electric vehicles, and renewable energy systems. The solid-electrolyte interphase (SEI) is a key component for the correct operation, performance, and safety of LIBs. The SEI arises from the initial thermal metastability of the anode-electrolyte interface, and the resulting electrolyte reduction products stabilize the interface by forming an electrochemical buffer window. This article aims to make a first-but important-step towards enhancing the parametrization of a widely-used reactive force field (ReaxFF) for accurate molecular dynamics (MD) simulations of SEI components in LIBs. To this end, we focus on Lithium Fluoride (LiF), an inorganic salt of great interest due to its beneficial properties in the passivation layer. The protocol relies heavily on various Python libraries designed to work with atomistic simulations allowing robust automation of all the reparameterization steps. The proposed set of configurations, and the resulting dataset, allow the new ReaxFF to recover the solid nature of the inorganic salt and improve the mass transport properties prediction from MD simulation. The optimized ReaxFF surpasses the previously available force field by accurately adjusting the diffusivity of lithium in the solid lattice, resulting in a two-order-of-magnitude improvement in its prediction at room temperature. However, our comprehensive investigation of the simulation shows the strong sensitivity of the ReaxFF to the training set, making its ability to interpolate the potential energy surface challenging. Consequently, the current formulation of ReaxFF can be effectively employed to model specific and well-defined phenomena by utilizing the proposed interactive reparameterization protocol to construct the dataset. Overall, this work represents a significant initial step towards refining ReaxFF for precise reactive MD simulations, shedding light on the challenges and limitations of ReaxFF force field parametrization. The demonstrated limitations emphasize the potential for developing more versatile and advanced force fields to upscale ab initio simulation through our interactive reparameterization protocol, enabling more accurate and comprehensive MD simulations in the future.

A novel concept of photosynthetic soft membranes: a numerical study.

We focus on a novel concept of photosynthetic soft membranes, possibly able to allow the conversion of solar energy and carbon dioxide (CO[Formula: see text]) into green fuels. The considered membranes rely on self-assembled functional molecules in the form of soap films. We elaborate a multi-scale and multi-physics model to describe the relevant phenomena, investigating the expected performance of a single soft photosynthetic membrane. First, we present a macroscale continuum model, which accounts for the transport of gaseous and ionic species within the soap film, the chemical equilibria and the two involved photocatalytic half reactions of the CO[Formula: see text] reduction and water oxidation at the two gas-surfactant-water interfaces of the soap film. Second, we introduce a mesoscale discrete Monte Carlo model, to deepen the investigation of the structure of the functional monolayers. Finally, the morphological information obtained at the mesoscale is integrated into the continuum model in a multi-scale framework. The developed tools are then used to perform sensitivity studies in a wide range of possible experimental conditions, to provide scenarios on fuel production by such a novel approach.

The impact of physicochemical features of carbon electrodes on the capacitive performance of supercapacitors: a machine learning approach.

Hybrid electric vehicles and portable electronic systems use supercapacitors for energy storage owing to their fast charging/discharging rates, long life cycle, and low maintenance. Specific capacitance is regarded as one of the most important performance-related characteristics of a supercapacitor's electrode. In the current study, Machine Learning (ML) algorithms were used to determine the impact of various physicochemical properties of carbon-based materials on the capacitive performance of electric double-layer capacitors. Published experimental datasets from 147 references (4899 data entries) were extracted and then used to train and test the ML models, to determine the relative importance of electrode material features on specific capacitance. These features include current density, pore volume, pore size, presence of defects, potential window, specific surface area, oxygen, and nitrogen content of the carbon-based electrode material. Additionally, categorical variables as the testing method, electrolyte, and carbon structure of the electrodes are considered as well. Among five applied regression models, an extreme gradient boosting model was found to best correlate those features with the capacitive performance, highlighting that the specific surface area, the presence of nitrogen doping, and the potential window are the most significant descriptors for the specific capacitance. These findings are summarized in a modular and open-source application for estimating the capacitance of supercapacitors given, as only inputs, the features of their carbon-based electrodes, the electrolyte and testing method. In perspective, this work introduces a new wide dataset of carbon electrodes for supercapacitors extracted from the experimental literature, also giving an instance of how electrochemical technology can benefit from ML models.

Mesoscopic Modeling and Experimental Validation of Thermal and Mechanical Properties of Polypropylene Nanocomposites Reinforced By Graphene-Based Fillers.

The development of nanocomposites relies on structure-property relations, which necessitate multiscale modeling approaches. This study presents a modeling framework that exploits mesoscopic models to predict the thermal and mechanical properties of nanocomposites starting from their molecular structure. In detail, mesoscopic models of polypropylene (PP)- and graphene-based nanofillers (graphene (Gr), graphene oxide (GO), and reduced graphene oxide (rGO)) are considered. The newly developed mesoscopic model for the PP/Gr nanocomposite provides mechanistic information on the thermal and mechanical properties at the filler-matrix interface, which can then be exploited to enhance the prediction accuracy of traditional continuum simulations by calibrating the thermal and mechanical properties of the filler-matrix interface. Once validated through a dedicated experimental campaign, this multiscale model demonstrates that with the modest addition of nanofillers (up to 2 wt %), the Young's modulus and thermal conductivity show up to 35 and 25% enhancement, respectively, whereas the Poisson's ratio slightly decreases. Among the different combinations tested, the PP/Gr nanocomposite shows the best mechanical properties, whereas PP/rGO demonstrates the best thermal conductivity. This validated mesoscopic model can contribute to the development of smart materials with enhanced mechanical and thermal properties based on polypropylene, especially for mechanical, energy storage, and sensing applications.

Assembling Biocompatible Polymers on Gold Nanoparticles: Toward a Rational Design of Particle Shape by Molecular Dynamics.

Gold nanoparticles (AuNPs) have received great attention in a number of fields ranging from the energy sector to biomedical applications. As far as the latter is concerned, due to rapid renal clearance and a short lifetime in blood, AuNPs are often encapsulated in a poly(lactic-co-glycolic acid) (PLGA) matrix owing to its biocompatibility and biodegradability. A better understanding of the PLGA polymers on the AuNP surface is crucial to improve and optimize the above encapsulation process. In this study, we combine a number of computational approaches to explore the adsorption mechanisms of PLGA oligomers on a Au crystalline NP and to rationalize the PLGA coating process toward a more efficient design of the NP shape. Atomistic simulations supported by a recently developed unsupervised machine learning scheme show the temporal evolution and behavior of PLGA clusterization by tuning the oligomer concentration in aqueous solutions. Then, a detailed surface coverage analysis coupled with free energy landscape calculations sheds light on the anisotropic nature of PLGA adsorption onto the AuNP. Our results prove that the NP shape and topology may address and privilege specific sites of adsorption, such as the Au {1 1 1} crystal planes in selected NP samples. The modeling-based investigation suggested in this article offers a solid platform to guide the design of coated NPs.

Multi-Scale Modelling of Aggregation of TiO2 Nanoparticle Suspensions in Water.

Titanium dioxide nanoparticles have risen concerns about their possible toxicity and the European Food Safety Authority recently banned the use of TiO2 nano-additive in food products. Following the intent of relating nanomaterials atomic structure with their toxicity without having to conduct large-scale experiments on living organisms, we investigate the aggregation of titanium dioxide nanoparticles using a multi-scale technique: starting from ab initio Density Functional Theory to get an accurate determination of the energetics and electronic structure, we switch to classical Molecular Dynamics simulations to calculate the Potential of Mean Force for the connection of two identical nanoparticles in water; the fitting of the latter by a set of mathematical equations is the key for the upscale. Lastly, we perform Brownian Dynamics simulations where each nanoparticle is a spherical bead. This coarsening strategy allows studying the aggregation of a few thousand nanoparticles. Applying this novel procedure, we find three new molecular descriptors, namely, the aggregation free energy and two numerical parameters used to correct the observed deviation from the aggregation kinetics described by the Smoluchowski theory. Ultimately, molecular descriptors can be fed into QSAR models to predict the toxicity of a material knowing its physicochemical properties, enabling safe design strategies.

Machine learning and materials modelling interpretation of in vivo toxicological response to TiO2 nanoparticles library (UV and non-UV exposure).

Assessing the risks of nanomaterials/nanoparticles (NMs/NPs) under various environmental conditions requires a more systematic approach, including the comparison of effects across many NMs with identified different but related characters/descriptors. Hence, there is an urgent need to provide coherent (eco)toxicological datasets containing comprehensive toxicity information relating to a diverse spectra of NPs characters. These datasets are test benches for developing holistic methodologies with broader applicability. In the present study we assessed the effects of a custom design Fe-doped TiO2 NPs library, using the soil invertebrate Enchytraeus crypticus (Oligochaeta), via a 5-day pulse via aqueous exposure followed by a 21-days recovery period in soil (survival, reproduction assessment). Obviously, when testing TiO2, realistic conditions should include UV exposure. The 11 Fe-TiO2 library contains NPs of size range between 5-27 nm with varying %Fe (enabling the photoactivation of TiO2 at energy wavelengths in the visible-light range). The NPs were each described by 122 descriptors, being a mixture of measured and atomistic model descriptors. The data were explored using single and univariate statistical methods, combined with machine learning and multiscale modelling techniques. An iterative pruning process was adopted for identifying automatically the most significant descriptors. TiO2 NPs toxicity decreased when combined with UV. Notably, the short-term water exposure induced lasting biological responses even after longer-term recovery in clean exposure. The correspondence with Fe-content correlated with the band-gap hence the reduction of UV oxidative stress. The inclusion of both measured and modelled materials data benefitted the explanation of the results, when combined with machine learning.

Cementitious composite materials for thermal energy storage applications: a preliminary characterization and theoretical analysis.

The lack of robust and low-cost sorbent materials still represents a formidable technological barrier for long-term storage of (renewable) thermal energy and more generally for Adsorptive Heat Transformations-AHT. In this work, we introduce a novel approach for synthesizing cement-based composite sorbent materials. In fact, considering the number of available hygrosopic salts that can be accommodated into a cementitious matrix-whose morphological properties can be also fine-tuned-the new proposed in situ synthesis paves the way to the generation of an entire new class of possible sorbents for AHT. Here, solely focusing on magnesium sulfate in a class G cement matrix, we show preliminary morphological, mechanical and calorimetric characterization of sub-optimal material samples. Our analysis enables us to theoretically estimate one of the most important figures of merit for the considered applications, namely the energy density which was found to range within 0.088-0.2 GJ/m3 (for the best tested sample) under reasonable operating conditions for space heating applications and temperate climate. The above estimates are found to be lower than other composite materials in the literature. Nonetheless, although no special material optimization has been implemented, our samples already compare favourably with most of the known materials in terms of specific cost of stored energy. Finally, an interesting aspect is found in the ageing tests under water sorption-desorption cycling, where a negligible variation in the adsorption capability is demonstrated after over one-hundred cycles.

Multistage and passive cooling process driven by salinity difference.

Space cooling in buildings is anticipated to rise because of an increasing thermal comfort demand worldwide, and this calls for cost-effective and sustainable cooling technologies. We present a proof-of-concept multistage device, where a net cooling capacity and a temperature difference are demonstrated as long as two water solutions at disparate salinity are maintained. Each stage is made of two hydrophilic layers separated by a hydrophobic membrane. An imbalance in water activity in the two layers naturally causes a non-isothermal vapor flux across the membrane without requiring any mechanical ancillaries. One prototype of the device developed a specific cooling capacity of up to 170 W m-2 at a vanishing temperature difference, considering a 3.1 mol/kg calcium chloride solution. To provide perspective, if successfully up-scaled, this concept may help satisfy at least partially the cooling needs in hot, humid regions with naturally available salinity gradients.

Mechanistic correlation between water infiltration and framework hydrophilicity in MFI zeolites.

Hydrophobic zeolites are nanoporous materials that are attracting an increasing interest, especially for catalysis, desalination, energy storage and biomedical applications. Nevertheless, a more profound understanding and control of water infiltration in their nanopores is still desirable to rationally design zeolite-based materials with tailored properties. In this work, both atomistic simulations and previous experimental data are employed to investigate water infiltration in hydrophobic MFI zeolites with different concentration of hydrophilic defects. Results show that limited concentrations of defects (e.g. 1%) induce a change in the shape of infiltration isotherms (from type-V to type-I), which denotes a sharp passage from typical hydrophobic to hydrophilic behavior. A correlation parametrized on both energy and geometric characteristics of the zeolite (infiltration model) is then adopted to interpolate the infiltration isotherms data by means of a limited number of physically-meaningful parameters. Finally, the infiltration model is combined with the water-zeolite interaction energy computed by simulations to correlate the water intrusion mechanism with the atomistic details of the zeolite crystal, such as defects concentration, distribution and hydrophilicity. The suggested methodology may allow a faster (more than one order of magnitude) and more systematic preliminary computational screening of innovative zeolite-based materials for energy storage, desalination and biomedical purposes.

Coffee-based colloids for direct solar absorption.

Despite their promising thermo-physical properties for direct solar absorption, carbon-based nanocolloids present some drawbacks, among which the unpleasant property of being potentially cytotoxic and harmful to the environment. In this work, a sustainable, stable and inexpensive colloid based on coffee is synthesized and its photo-thermal properties investigated. The proposed colloid consists of distilled water, Arabica coffee, glycerol and copper sulphate, which provide enhanced properties along with biocompatibility. The photo-thermal performance of the proposed fluid for direct solar absorption is analysed for different dilutions and compared with that of a traditional flat-plate collector. Tailor-made collectors, opportunely designed and realized via 3D-printing technique, were used for the experimental tests. The results obtained in field conditions, in good agreement with two different proposed models, show similar performance of the volumetric absorption using the proposed coffee-based colloids as compared to the classical systems based on a highly-absorbing surface. These results may encourage further investigations on simple, biocompatible and inexpensive colloids for direct solar absorption.

From GROMACS to LAMMPS: GRO2LAM : A converter for molecular dynamics software.

Atomistic simulations have progressively attracted attention in the study of physical-chemical properties of innovative nanomaterials. GROMACS and LAMMPS are currently the most widespread open-source software for molecular dynamics simulations thanks to their good flexibility, numerous functionalities and responsive community support. Nevertheless, the very different formats adopted for input and output files are limiting the possibility to transfer GROMACS simulations to LAMMPS. In this article, we present GRO2LAM, a modular and open-source Python 2.7 code for rapidly translating input files and parameters from GROMACS to LAMMPS format. The robustness of the tool has been assessed by comparing the simulation results obtained by GROMACS and LAMMPS, after the format conversion by GRO2LAM. Specifically, three nanoscale configurations of interest in both engineering and biomedical fields are studied, namely a carbon nanotube, an iron oxide nanoparticle, and a protein immersed in water. In perspective, GRO2LAM may be the first step to achieve a full interoperability between molecular dynamics software. This would allow to easily exploit their complementary potentialities and post-processing functionalities. Moreover, GRO2LAM could facilitate the cross-check of simulation results, guaranteeing the reproducibility of molecular dynamics models and testing their robustness. Graphical Abstract GRO2LAM, a modular and open-source Python code for rapidly translating input files and parameters from GROMACS to LAMMPS format.