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1. Intramolecular Relaxation Dynamics Mediated by Solvent–Solute Interactions of Substituted Fluorene Derivatives. Solute Structural Dependence

   https://doi.org/10.1021/acs.jpcb.1c06475  

   Capistran, Briana A.; Yuwono, Stephen H.; Moemeni, Mehdi; Maity, Soham; Vahdani, Aria; Borhan, Babak; Jackson, James E.; Piecuch, Piotr; Dantus, Marcos; Blanchard, G. J. (November 2021, The Journal of Physical Chemistry B)
2. Improving Stream Solute Predictions With a Modified LSTM Model Incorporating Solute Interdependences and Hysteresis Patterns

   https://doi.org/10.1029/2024JH000383  

   Agrawal, Tarun; Goodwell, Allison; Kumar, Praveen (February 2025, Journal of Geophysical Research: Machine Learning and Computation)

   Abstract Surface runoff and infiltrated water en route to the stream interact with dynamic landscape properties, ranging from vegetation and microbial activities to soil and geological attributes. Stream solute concentrations are highly variable and interconnected due to these interactions, flow paths, and residence times, and often exhibit hysteresis with flow. Significant unknowns remain about how point measurements of stream solute chemistry reflect interdependent hydrobiogeochemical and physical processes, and how signatures are encapsulated as nonlinear dynamical relationships between variables. We take a Machine Learning (ML) approach to understand and capture these dynamical relationships and improve predictions of solutes at short and long time scales. We introduce a physical process‐based “flow‐gate” into an Long Short‐Term Memory (LSTM) model, which enables the model to learn hysteresis behaviors if they exist. Further, we use information‐theoretic metrics to detect how solutes are interdependent and iteratively select source solutes that best predict a given target solute concentration. The “flow‐gate LSTM” model improves model predictions (1%–32% decreases in RMSE) relative to the standard LSTM model for all nine solutes included in the study. The predictive improvements from the flow‐gate LSTM model highlight the importance of lagged concentration and discharge relationships for certain solutes. It also indicates a potential limitation in the traditional LSTM model approach since flow rates are always provided as input sources, but this information is not fully utilized. This work provides a starting point for a predictive understanding of geochemical interdependencies using machine‐learning approaches and highlights potential improvements in model architecture. 

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3. Diffusiophoresis in one-dimensional solute gradients

   https://doi.org/10.1039/c7sm01588g  

   Ault, Jesse T.; Warren, Patrick B.; Shin, Sangwoo; Stone, Howard A. (January 2017, Soft Matter)

   The diffusiophoretic motion of suspended colloidal particles under one-dimensional solute gradients is solved using numerical and analytical techniques. Similarity solutions are developed for the injection and withdrawal dynamics of particles into semi-infinite pores. Furthermore, a method of characteristics formulation of the diffusion-free particle transport model is presented and integrated to realize particle trajectories. Analytical solutions are presented for the limit of small particle diffusiophoretic mobility Γ p relative to the solute diffusivity D s for particle motions in both semi-infinite and finite domains. Results confirm the build up of local maxima and minima in the propagating particle front dynamics. The method of characteristics is shown to successfully predict particle motions and the position of the particle front, although it fails to accurately predict suspended particle concentrations in the vicinity of sharp gradients, such as at the particle front peak seen in some injection cases, where particle diffusion inevitably plays an important role. Results inform the design of applications in which the use of applied solute gradients can greatly enhance particle injection into and withdrawal from pores. 

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4. Precrystallization solute assemblies and crystal symmetry

   https://doi.org/10.1039/D1FD00080B  

   Warzecha, Monika; Verma, Lakshmanji; Chakrabarti, Rajshree; Hadjiev, Viktor G.; Florence, Alastair J.; Palmer, Jeremy C.; Vekilov, Peter G. (July 2022, Faraday Discussions)

   Solution crystallization is a part of the synthesis of materials ranging from geological and biological minerals to pharmaceuticals, fine chemicals, and advanced electronic components. Attempts to predict the structure, growth rates and properties of emerging crystals have been frustrated, in part, by the poor understanding of the correlations between the oligomeric state of the solute, the growth unit, and the crystal symmetry. To explore how a solute monomer or oligomer is selected as the unit that incorporates into kinks and how crystal symmetry impacts this selection, we combine scanning probe microscopy, optical spectroscopy, and all-atom molecular simulations using as examples two organic materials, olanzapine (OZPN) and etioporphyrin I (EtpI). The dominance of dimeric structures in OZPN crystals has spurred speculation that the dimers preform in the solution, where they capture the majority of the solute, and then assemble into crystals. By contrast, EtpI in crystals aligns in parallel stacks of flat EtpI monomers unrelated by point symmetry. Raman and absorption spectroscopies show that solute monomers are the majority solute species in solutions of both compounds. Surprisingly, the kinetics of incorporation of OZPN into kinks is bimolecular, indicating that the growth unit is a solute dimer, a minority solution component. The disconnection between the dominant solute species, the growth unit, and the crystal symmetry is even stronger with EtpI, for which the (010) face grows by incorporating monomers, whereas the growth unit of the (001) face is a dimer. Collectively, the crystallization kinetics results with OZPN and EtpI establish that the structures of the dominant solute species and of the incorporating solute complex do not correlate with the symmetry of the crystal lattice. In a broader context, these findings illuminate the immense complexity of crystallization scenarios that need to be explored on the road to the understanding and control of crystallization. 

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5. Quantifying Solute Partitioning in Phosphatidylcholine Membranes

   Gobrogge, Christine A. Walker (October 2017, Analytical chemistry)

   Time-resolved fluorescence measurements were used to characterize and quantify solute partitioning into 1,2- dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) lipid vesicles as a function of solute concentration and temperature. The solutes, coumarin 152 (C152) and coumarin 461 (C461), both belong to a family of 7-aminocoumarin dyes that have distinctive fluorescence lifetimes in different solvation environments. The two solutes differ in the 4-position where C152 has a trifluoromethyl group in place of C461’s -CH3 group. In vesicle containing solutions, multiexponential fluorescence decays imply separate solute populations in the aqueous buffer, solvated in the vesicle headgroup region and solvated in the acyl chain bilayer interior, respectively. Fluorescence amplitudes, corrected for differences in radiative rates, are used to calculate absolute partition coefficients and average number of solutes per vesicle as a function of coumarin:lipid ratio and average number of solutes per vesicle. Results show that C152 has an ∼10-fold greater affinity than C461 for lipid bilayers, despite both solutes having similar hydrophobicities as inferred from their log(P) values. Temperature-dependent partitioning data are used to calculate enthalpies and entropies of C152 partitioning as a function of concentration. These values are used to extrapolate to the infinitely dilute limit. Above and below the lipid gel−liquid crystalline temperature, partitioning is exothermic with negative changes in entropy. In the vicinity of the transition temperature, these quantities change sign with ΔHpart becoming endothermic (+70 kJ/mol) and entropically favored (ΔSpart = +240 J/(mol·K)). 

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