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1. Automated membrane characterization: In-situ monitoring of the permeate and retentate solutions using a 3D printed permeate probe device

   https://doi.org/10.1016/j.memlet.2024.100087  

   Ouimet, Jonathan Aubuchon; Al-Badani, Faraj; Liu, Xinhong; Lair, Laurianne; Muetzel, Zachary W; Dowling, Alexander W; Phillip, William A (December 2024, Journal of Membrane Science Letters)

   Self-driving laboratories and automated experiments can accelerate the design workflow and decrease errors associated with experiments that characterize membrane transport properties. Within this study, we use 3D printing to design a custom stirred cell that incorporates inline conductivity probes in the retentate and permeate streams. The probes provide a complete trajectory of the salt concentrations as they evolve over the course of an experiment. Here, automated diafiltration experiments are used to characterize the performance of commercial NF90 and NF270 polyamide membranes over a predetermined range of KCl concentrations from 1 to 100 mM. The measurements obtained by the inline conductivity probes are validated using offline post-experiment analyses. Compared to traditional filtration experiments, the probes decrease the amount of time required for an experimentalist to characterize membrane materials by more than 50×and increase the amount of information generated by 100×. Device design principles to address the physical constraints associated with making conductivity measurements in confined volumes are proposed. Overall, the device developed within this study provides a foundation to establish high-throughput, automated membrane characterization techniques. 

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2. Molecular Design for Optically Induced Magnetization: Targeting Excited State Orbital Degeneracy in Tungsten(V) Complexes

   https://doi.org/10.1021/jacs.5c03783  

   Ramsier, Ian E; Mandato, Alysia; Saxena, Sunil; Transue, Wesley J (June 2025, Journal of the American Chemical Society)

   The rise of quantum information science has spurred chemists to prepare new molecules that serve as useful building blocks in quantum technologies of the future. Implementation of molecular spin-based qubits requires new methods to induce high spin polarization of samples. Herein, we report design criteria to develop axially symmetric spin-1/2 molecules amenable to optically induced magnetization (OIM), a technique using circularly polarized (CP) excitation to deliver spin polarization. We apply these criteria to develop a series of tungsten(V) chalcogenide complexes that are demonstrated to have large spin-sensitive responses to CP light using magnetic circular dichroism (MCD) that could allow up to ∼20% spin polarization through OIM. Pulsed electron paramagnetic resonance (EPR) spectra reveal these systems have improved relaxation times over molecules like K2IrCl6, a species recently investigated by OIM, and field-swept electron spin−echo (FS-ESE) experiments show they have a remarkable lack of anisotropy in their phase-memory Tm times. The design criteria are general and point toward future ways to improve OIMinitializable qubits. 

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3. Mathematical Modelling of Reactive Inks for Additive Manufacturing of Charged Membranes

   Liu, X.; De, R.; Perez, A.; Hoffman, J.R.; Phillip, W.A.; Dowling, A.W. (July 2022, Computer aided chemical engineering)

   Yamashita, Y.; Kano, M. (Ed.)

   Patterned charged membranes with engendered useful characteristics can offer selective transport of electrolytes. Chemical patterning across the membrane surface via a physical inkjet deposition process requires precise control of the reactive-ink formulation, which enables the introduction of charged functionality to the membrane. This study develops a new dynamic mathematical model for the primary step of the batch reactive-ink formulation considering an ink mixture of copper sulphate and ascorbic acid. Nonlinear least squares parameter estimation is performed to infer three kinetic model parameters by analysing data from nine dynamic experiments simultaneously. Global sensitivity and Fisher information matrix (FIM) analyses reveal only one kinetic parameter is identifiable from time-series pH measurements. The fitted model can capture the overall nonlinear dynamics of the batch reaction and works best for initial Cu2 + concentrations between 30 and 50 mM. Time-series Cu2 + or Cu+ concentration measurements are recommended in future experiments to elucidate the kinetics of reactive-ink formulation. 

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4. Buffer effects on protein sieving losses in ultrafiltration and their relationship to biophysical properties

   https://doi.org/10.1002/btpr.3481  

   Mohammadzadehmarandi, Aylin; Zydney, Andrew_L (May 2024, Biotechnology Progress)

   Abstract The design of effective ultrafiltration/diafiltration processes for protein formulation requires the use of membranes with very high protein retention. The objective of this study was to examine the effects of specific buffers on the retention of a model protein (bovine serum albumin) during ultrafiltration. Albumin retention at pH 4.8 was significantly reduced in phosphate buffer compared with that in acetate, citrate, and histidine. This behavior was consistent with a small change in the effective albumin hydrodynamic diameter as determined by dynamic light scattering. The underlying conformational changes leading to this change in diameter were explored using circular dichroism spectroscopy and differential scanning calorimetry. These results provide important insights into the factors controlling protein retention during ultrafiltration and diafiltration. 

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5. Optimal Bayesian supervised domain adaptation for RNA sequencing data

   https://doi.org/10.1093/bioinformatics/btab228  

   Boluki, Shahin; Qian, Xiaoning; Dougherty, Edward R (April 2021, Bioinformatics)

   Gorodkin, Jan (Ed.)

   Abstract Motivation When learning to subtype complex disease based on next-generation sequencing data, the amount of available data is often limited. Recent works have tried to leverage data from other domains to design better predictors in the target domain of interest with varying degrees of success. But they are either limited to the cases requiring the outcome label correspondence across domains or cannot leverage the label information at all. Moreover, the existing methods cannot usually benefit from other information available a priori such as gene interaction networks. Results In this article, we develop a generative optimal Bayesian supervised domain adaptation (OBSDA) model that can integrate RNA sequencing (RNA-Seq) data from different domains along with their labels for improving prediction accuracy in the target domain. Our model can be applied in cases where different domains share the same labels or have different ones. OBSDA is based on a hierarchical Bayesian negative binomial model with parameter factorization, for which the optimal predictor can be derived by marginalization of likelihood over the posterior of the parameters. We first provide an efficient Gibbs sampler for parameter inference in OBSDA. Then, we leverage the gene-gene network prior information and construct an informed and flexible variational family to infer the posterior distributions of model parameters. Comprehensive experiments on real-world RNA-Seq data demonstrate the superior performance of OBSDA, in terms of accuracy in identifying cancer subtypes by utilizing data from different domains. Moreover, we show that by taking advantage of the prior network information we can further improve the performance. Availability and implementation The source code for implementations of OBSDA and SI-OBSDA are available at the following link. https://github.com/SHBLK/BSDA. Supplementary information Supplementary data are available at Bioinformatics online. 

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