Open Access Publications

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Open access publications by faculty, postdocs, and graduate students in the Department of Chemical and Biomolecular Engineering

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A Benign Synthesis Route to Terephthalic Acid via Two-Step Electrochemical Oxidation of P-xylene
(Journal of The Electrochemical Society, 2024-05-31) Ding, Haoran; Orazov, Marat; Oliveira, Nicholas; Yan, Yushan
Terephthalic acid is conventionally synthesized through the AMOCO process under harsh conditions, making milder electrosynthesis routes desirable. Electrooxidation of p-xylene has been demonstrated but the degree of oxidation is limited, resulting in low terephthalic acid yields. Here, we demonstrate a process with two electrochemical steps enabling the complete oxidation of p-xylene into terephthalic acid. The first electrochemical step achieves C-H activation of p-xylene using electrochemically generated bromine as a mediator, while the second electrochemical step does alcohol oxidation of 1,4-benzenedimethanol into terephthalate on NiOOH. The divided cell in the first step simultaneously generates acid and base that are utilized subsequently, negating the need of external acid and base addition and thus offering a cost competitive synthesis route. The competing bromide oxidation in the second step is suppressed by using constant voltage electrolysis at 0.50 V, where an optimal yield of terephthalic acid of 81% is achieved.
A color prediction model for mending materials of the Yuquan Iron Pagoda in China based on machine learning
(Heritage Science, 2024-06-06) Liu, Xuegang; Liu, Yuhang; Wang, Ke; Zhang, Yang; Lei, Yang; An, Hai; Wang, Mingqiang; Chen, Yuqiu
During the restoration of iron cultural relics, the removal of rust from these artifacts is necessary. However, this rust removal process may lead to inconsistent local color on the iron relics. To address this, mending materials are applied to treat the surface, ensuring consistent local color. In the surface treatment of iron cultural relics, a significant challenge lies in modulating the color of these mending materials. The corrosion products of Yuquan Iron Pagoda are mainly Fe3O4, γ-FeO(OH), α-FeO(OH) and α-Fe2O3, with contents of 13.1, 16.1, 40.2 and 30.6%, respectively. Due to their structural stability and suitable color characteristics, Fe3O4 and α-Fe2O3 are selected as the primary raw materials for the repair material. This study employs machine learning methods to predict the color of mending materials corresponding to varying contents of α-Fe2O3, Fe3O4, and epoxy resin. The Artificial Neural Network (ANN), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boost Machine (LightGBM) algorithms are utilized to develop the model, and the predictive performance of these three algorithms is compared. XGBoost exhibits the best prediction performance, achieving a square correlation coefficient (R2) of 0.94238 and a mean absolute error (MAE) of 0.68485. Additionally, the SHapley Additive exPlanations (SHAP) method is employed to analyze the most crucial raw material affecting the color of mending materials, which is identified as Fe3O4. The study illustrates the specific process of employing this model by applying it to the surface treatment of the Yuquan Iron Pagoda, demonstrating the practicality of the model. This model can be applied to assist in the surface treatment of other iron cultural relics.
A computational method for rapid analysis polymer structure and inverse design strategy (RAPSIDY)
(Soft Matter, 2024-09-30) Liao, Vinson; Myers, Tristan; Jayaraman, Arthi
Tailoring polymers for target applications often involves selecting candidates from a large design parameter space including polymer chemistry, molar mass, sequence, and architecture, and linking each candidate to their assembled structures and in turn their properties. To accelerate this process, there is a critical need for inverse design of polymers and fast exploration of the structures they can form. This need has been particularly challenging to fulfill due to the multiple length scales and time scales of structural arrangements found in polymers that together give rise to the materials’ properties. In this work, we tackle this challenge by introducing a computational framework called RAPSIDY – Rapid Analysis of Polymer Structure and Inverse Design strategY. RAPSIDY enables inverse design of polymers by accelerating the evaluation of stability of multiscale structure for any given polymer design (sequence, composition, length). We use molecular dynamics simulations as the base method and apply a guiding potential to initialize polymers chains of a selected design within target morphologies. After initialization, the guiding potential is turned off, and we allow the chains and structure to relax. By evaluating similarity between the target morphology and the relaxed morphology for that polymer design, we can screen many polymer designs in a highly parallelized manner to rank designs that are likely to remain in that target morphology. We demonstrate how this method works using an example of a symmetric, linear pentablock, AxByAzByAx, copolymer system for which we determine polymer sequences that exhibit stable double gyroid morphology. Rather than trying to identify the global free-energy minimum morphology for a specific polymer design, we aim to identify candidates of polymer design parameter space that are more stable in the desired morphology than others. Our approach reduces computational costs for design parameter exploration by up to two orders-of-magnitude compared to traditional MD methods, thus accelerating design and engineering of novel polymer materials for target applications.
A Genetic Engineering Toolbox for the Lignocellulolytic Anaerobic Gut Fungus Neocallimastix frontalis
(ACS Synthetic Biology, 2023-04-21) Hooker, Casey, A.; Hanafy, Radwa; Hillman, Ethan T.; Muñoz Briones, Javier; Solomon, Kevin V.
Anaerobic fungi are powerful platforms for biotechnology that remain unexploited due to a lack of genetic tools. These gut fungi encode the largest number of lignocellulolytic carbohydrate active enzymes (CAZymes) in the fungal kingdom, making them attractive for applications in renewable energy and sustainability. However, efforts to genetically modify anaerobic fungi have remained limited due to inefficient methods for DNA uptake and a lack of characterized genetic parts. We demonstrate that anaerobic fungi are naturally competent for DNA and leverage this to develop a nascent genetic toolbox informed by recently acquired genomes for transient transformation of anaerobic fungi. We validate multiple selectable markers (HygR and Neo), an anaerobic reporter protein (iRFP702), enolase and TEF1A promoters, TEF1A terminator, and a nuclear localization tag for protein compartmentalization. This work establishes novel methods to reliably transform the anaerobic fungus Neocallimastix frontalis, thereby paving the way for strain development and various synthetic biology applications.
A mathematical modeling approach for supply chain management under disruption and operational uncertainty
(AIChE Journal, 2023-03-21) Badejo, Oluwadare; Ierapetritou, Marianthi
In this work, we proposed a two-stage stochastic programming model for a four-echelon supply chain problem considering possible disruptions at the nodes (supplier and facilities) as well as the connecting transportation modes and operational uncertainties in form of uncertain demands. The first stage decisions are supplier choice, capacity levels for manufacturing sites and warehouses, inventory levels, transportation modes selection, and shipment decisions for the certain periods, and the second stage anticipates the cost of meeting future demands subject to the first stage decision. Comparing the solution obtained for the two-stage stochastic model with a multi-period deterministic model shows that the stochastic model makes a better first stage decision to hedge against the future demand. This study demonstrates the managerial viability of the proposed model in decision making for supply chain network in which both disruption and operational uncertainties are accounted for.
A novel digital lifecycle for Material-Process-Microstructure-Performance relationships of thermoplastic olefins foams manufactured via supercritical fluid assisted foam injection molding
(Polymer Engineering and Science, 2024-03-15) Pradeep, Sai Aditya; Deshpande, Amit M.; Lavertu, Pierre‐Yves; Zheng, Ting; Yerra, Veera Aditya; Shimabukuro, Yiro; Li, Gang; Pilla, Srikanth
This research significantly enhances the applicability of thermoplastic olefins (TPOs) in the automotive industry using supercritical N2 as a physical foaming agent, effectively addressing the limitations of traditional chemical agents. It merges experimental results with simulations to establish detailed material-process-microstructure-performance (MP2) relationships, targeting 5–20% weight reductions. This innovative approach labeled digital lifecycle (DLC) helps accurately predict tensile, flexural, and impact properties based on the foam microstructure, along with experimentally demonstrating improved paintability. The study combines process simulations with finite element models to develop a comprehensive digital model for accurately predicting mechanical properties. Our findings demonstrate a strong correlation between simulated and experimental data, with about a 5% error across various weight reduction targets, marking significant improvements over existing analytical models. This research highlights the efficacy of physical foaming agents in TPO enhancement and emphasizes the importance of integrating experimental and simulation methods to capture the underlying foaming mechanism to establish material-process-microstructure-performance (MP2) relationships. Highlights - Establishes a material-process-microstructure-performance (MP2) for TPO foams - Sustainably produces TPO foams using supercritical (ScF) N2 with 20% lightweighting - Shows enhanced paintability for TPO foam improved surface aesthetics - Digital lifecycle (DLC) that predicts both foam microstructure and properties - DLC maps process effects & microstructure onto FEA mesh for precise prediction
A polydisperse model for thixotropic elasto-viscoplastic suspensions of aggregating particles using population balances
(AIChE Journal, 2023-09-18) Jariwala, Soham; Song, Rong; Hipp, Julie B.; Diemer, R. Bertrum; Wagner, Norman J.; Beris, Antony N.
An improved population balance-based rheological constitutive framework for polydisperse aggregating suspensions is derived by incorporating detailed models for orthokinetic and perikinetic aggregation and shear breakage processes. The framework accounts for critical properties such as dynamic arrest, viscoelasticity, kinematic hardening, thixotropy, and yield stress to generate a full range of thixotropic elasto-viscoplastic (TEVP) response. Additionally, the model is thermodynamically consistent because the dynamics and timescales are completely determined by internal structural and kinetic variables. The model connects the rheological response to the structural descriptors such as the size distribution of agglomerates, mean sizes, fractal dimension, and agglomerate volume fraction. Predictions are compared against a wide range of shear rheology measurements data for model thixotropic suspensions of fumed silica and carbon black, including large amplitude oscillatory shear (LAOS), as well as ultra-small angle neutron scattering under steady shear (Rheo-uSANS).
A stochastic model of solid state thin film deposition: Application to chalcopyrite growth
(Americanican Institute of Physics, 4/26/16) Lovelett,Robert J.; Pang,Xueqi; Roberts,Tyler M.; Shafarman,William N.; Birkmire,Robert W.; Ogunnaike,Babatunde A.; Robert J. Lovelett, Xueqi Pang, Tyler M. Roberts, William N. Shafarman,Robert W. Birkmire, and Babatunde A. Ogunnaike; Shafarman, William N; Birkmire, Robert W; Ogunnaike Babatunde A
Developmenteloping high fidelity quantitative models of solid state reaction systems can be challenging, especially in deposition systems where, in addition to the multiple competing processes occurring simultaneously, the solid interacts with its atmosphere. In this work, we Developmentelop a model for the growth of a thin solid film where species from the atmosphere adsorb, diffuse, and react with the film. The model is mesoscale and describes an entire film with thickness on the order of microns. Because it is stochastic, the model allows us to examine inhomogeneities and agglomerations that would be impossible to characterize with deterministic methods. We demonstrate the modeling approach with the example of chalcopyrite Cu(InGa)(SeS)(2) thin film growth via precursor reaction, which is a common industrial method for fabricating thin film photovoltaic modules. The model is used to understand how and why through-film variation in the composition of Cu(InGa)(SeS)(2) thin films arises and persists. We believe that the model will be valuable as an effective quantitative description of many other materials systems used in semiconductors, energy storage, and other fast-growing industries. (C) 2016 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Ab Initio Molecular Dynamics Study of Pt Clustering on γ-Al2O3 and Sn-Modified γ-Al2O3
(Journal of Physical Chemistry C, 2023-10-05) Chen, Tso-Hsuan; Vlachos, Dionisios G.; Caratzoulas, Stavros
We have conducted AIMD free energy simulations to examine the dynamics of Pt atoms and Ptn (n = 2–3) species on dry γ-Al2O3(100), dry γ-Al2O3(110), and wet γ-Al2O3(110) surfaces, with OH coverages corresponding to 500 K (11.8 OH/nm2) and 800 K (5.9 OH/nm2), while varying the Pt and Sn loading. Under the same dry conditions and temperature, comparing the (100) and (110) surface terminations revealed that the interactions between Pt and the surface play a crucial role in determining whether the potential of mean force between reduced Pt atoms is repulsive, as observed on the (100) surface, or if it can support a bound Pt–Pt state, as observed on the (110) surface. The hydration of the (110) surface had a significant impact. At a Pt loading of 0.75 Pt/nm2, with hydration of 5.9 OH/nm2, the energy of the potential of mean force increases. Although a Pt–Pt bound state is still supported, it becomes kinetically less accessible from the dispersed state. At an even higher water loading of 11.8 OH/nm2, the Pt–Pt potential of mean force becomes predominantly repulsive and can no longer sustain the Pt–Pt bound state. Higher Pt loadings of 1.12 Pt atoms/nm2 promote the aggregation of Pt into progressively larger clusters, but high levels of hydration can kinetically impede particle growth. On Sn-modified γ-Al2O3(110), Pt tends to associate with Sn, except at high levels of surface hydration where the potential of mean force between Pt and Sn atoms becomes repulsive. The presence of Sn inhibits the aggregation of Pt particles, and the Pt–Pt potential of mean force becomes increasingly repulsive with higher Sn loading.
Accelerating manufacturing for biomass conversion via integrated process and bench digitalization: a perspective
(Reaction Chemistry and Engineering, 2022-01-25) Batchu, Sai Praneet; Hernandez, Borja; Malhotra, Abhinav; Fang, Hui; Ierapetritou, Marianthi; Vlachos, Dionisios G.
We present a perspective for accelerating biomass manufacturing via digitalization. We summarize the challenges for manufacturing and identify areas where digitalization can help. A profound potential in using lignocellulosic biomass and renewable feedstocks, in general, is to produce new molecules and products with unmatched properties that have no analog in traditional refineries. Discovering such performance-advantaged molecules and the paths and processes to make them rapidly and systematically can transform manufacturing practices. We discuss retrosynthetic approaches, text mining, natural language processing, and modern machine learning methods to enable digitalization. Laboratory and multiscale computation automation via active learning are crucial to complement existing literature and expedite discovery and valuable data collection without a human in the loop. Such data can help process simulation and optimization select the most promising processes and molecules according to economic, environmental, and societal metrics. We propose the close integration between bench and process scale models and data to exploit the low dimensionality of the data and transform the manufacturing for renewable feedstocks.
Activation of Molecular Oxygen for Alcohol Oxidation over Vanadium Carbon Catalysts Synthesized via the Heterogeneous Ligand Strategy
(ACS Catalysis, 2022-12-16) Zhao, Li; Yang, Piaoping; Shi, Song; Zhu, Guozhi; Feng, Xiao; Zheng, Weiqing; Vlachos, Dionisios G.; Xu, Jie
Activation of molecular oxygen to realize selective oxidation is challenging. We employ a heterogeneous ligand method to prepare a vanadium carbon catalyst (V–C catalyst) of high efficiency in alcohol oxidation via oxygen activation. Principal component analysis revealed that the chemisorbed oxygen and pentavalent vanadium oxide are crucial in catalyst performance. Isotopic labeling, electron paramagnetic resonance, and control experiments confirmed that the V–C catalyst activates molecular oxygen to singlet oxygen or its analogue and carries out the reaction. A kinetic study and in situ React-IR spectra illustrated that the main reaction route is the O2 activation to 1O2 or its analogue and oxidizes the substrate through C–H bond activation. We demonstrate the efficiency of the V–C catalyst in selectively oxidizing the hydroxyl group in other substrates, including benzyl alcohols, methyl lactate, and ethyl lactate biomass-based alcohols. This will guide the development of highly active nonprecious metal catalysts for activating O2 for aerobic oxidation.
Advective Assembler-Enhanced Support Bath Rotational Direct Ink Writing
(Advanced Materials Technologies, 2024-05-01) Pleij, Tazio; Bayles, Alexandra V.; Vermant, Jan
Manufacturing intricately controlled, hierarchically distributed structures poses significant fabrication challenges, but is crucial for enhancing functionality in synthetic systems. A 3D printing technique combining advective assembly with rotational direct ink writing is developed and exploited to build topologically complex, multimaterial structures with high precision. A modular advective assembler printhead is fabricated and employed in the process. This flow-structuring device is designed with a complex network of internal channels that patterns flowing hydrogel-based inks, creating multi-layered filaments whose structures go well beyond conventional nozzle shape and size limitations. The composite filaments are extruded into a rotating support bath of Polyacrylic acid microgels. The rheology of the inks and support bath are critical to maintain print fidelity and integrity, and are characterized by linear and nonlinear bulk rheometry. Optimization of the materials creates a platform where curvilinear, multimaterial architectures are constructed without being constrained to slicing across X, Y, and Z axes. The versatility of this manufacturing platform is demonstrated by printing helical structures that undergo swelling-induced actuation. This processing method has the potential to significantly enhance additive manufacturing by enabling the production of intricate, multiscale composite structures with broad applicability in fields such as bioengineering, soft robotics, and functional composite materials.
Age-dependent changes in phagocytic activity: in vivo response of mouse pulmonary antigen presenting cells to direct lung delivery of charged PEGDA nanoparticles
(Journal of Nanobiotechnology, 2024-08-12) Sudduth, Emma R.; Ruiz, Aida López; Trautmann-Rodriguez, Michael; Fromen, Catherine A.
Background Current needle-based vaccination for respiratory viruses is ineffective at producing sufficient, long-lasting local immunity in the elderly. Direct pulmonary delivery to the resident local pulmonary immune cells can create long-term mucosal responses. However, criteria for drug vehicle design rules that can overcome age-specific changes in immune cell functions have yet to be established. Results Here, in vivo charge-based nanoparticle (NP) uptake was compared in mice of two age groups (2- and 16-months) within the four notable pulmonary antigen presenting cell (APC) populations: alveolar macrophages (AM), interstitial macrophages (IM), CD103+ dendritic cells (DCs), and CD11b+ DCs. Both macrophage populations exhibited preferential uptake of anionic nanoparticles but showed inverse rates of phagocytosis between the AM and IM populations across age. DC populations demonstrated preferential uptake of cationic nanoparticles, which remarkably did not significantly change in the aged group. Further characterization of cell phenotypes post-NP internalization demonstrated unique surface marker expression and activation levels for each APC population, showcasing heightened DC inflammatory response to NP delivery in the aged group. Conclusion The age of mice demonstrated significant preferences in the charge-based NP uptake in APCs that differed greatly between macrophages and DCs. Carefully balance of the targeting and activation of specific types of pulmonary APCs will be critical to produce efficient, age-based vaccines for the growing elderly population.
Analytical characterization of host-cell-protein-rich aggregates in monoclonal antibody solutions
(Biotechnology Progress, 2023-04-05) Herman, Chase E.; Min, Lie; Choe, Leila H.; Maurer, Ronald W.; Xu, Xuankuo; Ghose, Sanchayita; Lee, Kelvin H.; Lenhoff, Abraham M.
Host-cell proteins (HCPs) and high molecular weight (HMW) species have historically been treated as independent classes of impurities in the downstream processing of monoclonal antibodies (mAbs), but recent indications suggest that they may be partially linked. We have explored this connection with a shotgun proteomic analysis of HMW impurities that were isolated from harvest cell culture fluid (HCCF) and protein A eluate using size-exclusion chromatography (SEC). As part of the proteomic analysis, a cross-digest study was performed in which samples were analyzed using both the standard and native digest techniques to enable a fair comparison between bioprocess pools. This comparison reveals that the HCP profiles of HCCF and protein A eluate overlap substantially more than previous work has suggested, because hundreds of HCPs are conserved in aggregates that may be up to ~50 nm in hydrodynamic radius and that persist through the protein A capture step. Quantitative SWATH proteomics suggests that the majority of the protein A eluate's HCP mass is found in such aggregates, and this is corroborated by ELISA measurements on SEC fractions. The SWATH data also show that intra-aggregate concentrations of individual HCPs are positively correlated between aggregates that were isolated from HCCF and protein A eluate, and species that have generally been considered difficult to remove tend to be more concentrated than their counterparts. These observations support prior hypotheses regarding aggregate-mediated HCP persistence through protein A chromatography and highlight the importance of this persistence mechanism.
Anisotropy factors in small-angle scattering for dilute rigid-rod suspensions
(Journal of Applied Crystallography, 2023-06) Rooks, J.; Gilbert, P. H.; Porcar, L.; Liu, Y.; Butler, P.
Alignment of anisotropic particles along specific orientations influences the mechanical and rheological properties of a material. Small-angle scattering techniques are widely used to probe this alignment through analysis of anisotropic two-dimensional scattering intensity patterns. The anisotropy factor is the simplest and most common quantitative parameter for describing scattering anisotropy, especially in systems containing rod-like particles, and there are several methods for calculating this factor. However, there has been no systematic study comparing these methods while also evaluating the limitations imposed by non-idealities from instrumentation or polydisperse morphology. Three of the most common methods for calculating an anisotropy factor are examined here and their effectiveness for describing the orientation of a theoretical cylinder is evaluated. It is found that the maximum theoretical value of 1 for the anisotropy factor is only accessible at certain values of scattering vector q. The analysis details recommendations for q-range selection and data binning, as these influence the calculations. The theoretical results are supported by experimental small-angle neutron scattering data for a wormlike micelle solution undergoing shear, where different calculation methods yield distinct quantifications of anisotropy.
Anodically-Generated Alkyl Radicals Derived from Carboxylic Acids as Reactive Intermediates for Addition to Alkenes
(ChemElectroChem, 2023-05-12) Ding, Haoran; Orazov, Marat
Electrochemically driven C−C coupling has the potential to reduce the cost and environmental impact of some organic syntheses currently accomplished through thermochemical methods. Here, we use electrochemical oxidation of carboxylic acids as a source of reactive carbon-centered radicals that enable radical addition to alkenes in the anode boundary layer. We demonstrate an optimization of reaction conditions to suppress the thermodynamically favored, but synthetically undesirable radical self-coupling in favor of radical addition to styrene. In methanol solvent, 88 % selectivity and 72 % Faradaic efficiency for targeted functionalized benzenes are achieved. For low current densities, iridium anodes outperform platinum, gold, palladium, and glassy carbon anodes. With constant potential or constant current electrolyses, the deposition of organic by-products on the catalyst surface leads to anode passivation. We show that periodic cathodic current pulses effectively regenerate the catalyst. Lastly, we confirm the role of free radicals in the reaction mechanism with a radical trap. Graphical Abstract available at: https://doi.org/10.1002/celc.202201099 Electrochemically driven C−C coupling: High anodic current density favors the formation of the undesirable radical self-coupling product. Low anodic current density favors the formation of the target product, but also leads to more solvent oxidation. Solvent oxidation at low current density can be suppressed by multiple methods to achieve high Faradaic efficiency of the target product at high selectivity.
Anomalous rheological aging of a model thermoreversible colloidal gel following a thermal quench
(The Journal of Chemical Physics, 2022-06-15) Suman, Khushboo; Wagner, Norman J.
We investigate the aging behavior in a well-studied model system comprised of a colloidal suspension of thermoreversible adhesive hard spheres (AHS) but thermally quenched below the gel transition to much larger depths than previously studied. The aging behavior in the model AHS system is monitored by small amplitude oscillatory shear rheology measurements conducted while rapidly quenching from liquid state at 40{degree sign}C to a temperature below the gel temperature and new, anomalous aging behaviors are observed. Shallow quenches lead to monotonic development of the elastic modulus with time consistent with prior reports for the development of a homogeneous gel (Gordon et al., Journal of Rheology 2017). However, for deeper quenches, a unique and new phenomenon is reported - namely after an initial rise in the modulus, a reproducible drop in modulus is observed, followed by a plateau in modulus value. This drop can be gradual or sudden, and the extent of the drop, both depends on quench depth. After this drop in modulus, AHS gel evolves toward a quench-path independent state over the experimental timescale. These effects of the extent of quenching on aging behavior is hypothesized to be a consequence of quenching into different underlying thermodynamic states of colloidal gels and the possible influence of the adhesive glass dynamical arrest for the deepest quenches. The research connects homogeneous gelation with heterogeneous gel formation due to phase separation and shows that the extent of quench can be used as an independent parameter to govern the rheological response of the arrested gel.
Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
(Journal of Visualized Experiments, 2022-02-15) Borjigin, Tohn; Boddupalli, Anuraag; Sullivan, Millicent O.
Quantification of cells is necessary for a wide range of biological and biochemical studies. Conventional image analysis of cells typically employs either fluorescence detection approaches, such as immunofluorescent staining or transfection with fluorescent proteins or edge detection techniques, which are often error-prone due to noise and other non-idealities in the image background. We designed a new algorithm that could accurately count and distinguish macrophages and fibroblasts, cells of different phenotypes that often colocalize during tissue regeneration. MATLAB was used to implement the algorithm, which differentiated distinct cell types based on differences in height from the background. A primary algorithm was developed using an area-based method to account for variations in cell size/structure and high-density seeding conditions. Non-idealities in cell structures were accounted for with a secondary, iterative algorithm utilizing internal parameters such as cell coverage computed using experimental data for a given cell type. Finally, an analysis of coculture environments was carried out using an isolation algorithm in which various cell types were selectively excluded based on the evaluation of relative height differences within the image. This approach was found to accurately count cells within a 5% error margin for monocultured cells and within a 10% error margin for cocultured cells.
Best practices for electrochemical reduction of carbon dioxide
(Nature Sustainability, 2023-01-02) Seger, Brian; Robert, Marc; Jiao, Feng
Carbon capture, utilization and storage, a fundamental process to a sustainable future, relies on a suite of technologies among which electrochemical reduction of carbon dioxide is essential. Here, we discuss the issues faced when reporting performance of this technology and recommend how to move forward at both materials and device levels. Electrochemical reduction of CO2 into value-added chemicals has attracted considerable attention recently1,2,3. However, reporting the performance of a new CO2 electrocatalyst or a new reactor design is not trivial because of the complex nature of the CO2 electroreduction reaction. In many cases, the results are presented in a confusing manner, rendering it difficult to assess the true performance of the catalyst and/or device. In this Comment, we first discuss common problems in reporting the performance of a new electrocatalyst (including both heterogeneous and molecular catalysts) in the literature and then extend the discussion to how the products should be properly measured and quantified. Finally, we comment on the issues associated with full-cell level studies and recommend the best practices for electrochemical CO2 reduction.
Bioderived silicon nano-quills: synthesis, structure and performance in lithium-ion battery anodes
(Green Chemistry, 2024-03-12) Chen, Nancy; Sabet, Morteza; Sapkota, Nawraj; Parekh, Mihir; Chiluwal, Shailendra; Koehler, Kelliann; Clemons, Craig M.; Ding, Yi; Rao, Apparao M.; Pilla, Srikanth
Cellulose nanocrystals (CNCs) are bioderived one-dimensional species with versatile surface chemistry and unique self-assembling behavior in aqueous solutions. This work presents a scientific approach to leverage these characteristics for creating CNC network templates and processing them to engineer a novel silicon (Si)-based material called silicon nano-quill (SiNQ) for energy storage applications. The SiNQ structure possesses a porous, tubular morphology with a substantial ability to store lithium ions while maintaining its structural integrity. The presence of Si suboxides in the SiNQ structure is demonstrated to be crucial for realizing a stable cycling performance. One of the defining attributes of SiNQ is its water dispersibility due to Si–H surface bonds, promoting water-based Si-graphite electrode manufacturing with environmental and economic benefits. The incorporation of only 17 wt% SiNQ enhances the capacity of graphitic anodes by ∼2.5 times. An initial coulombic efficiency of 97.5% is achieved by employing a versatile pre-lithiation. The SiNQ-graphite anodes with high active loading, when subjected to accelerated charging/discharging conditions at 5.4 mA cm−2, exhibit stable cycling stability up to 500 cycles and average coulombic efficiency of >99%. A generalized physics-based cyclic voltammetry model is presented to explain the remarkable behavior of SiNQs under fast-charging conditions.

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