Modular Toolkit Research Articles

Expanded Functionality and Portability for the Colvars Library.

Colvars is an open-source C++ library that provides a modular toolkit for collective-variable-based molecular simulations. It allows practitioners to easily create and implement descriptors that best fit a process of interest and to apply a wide range of biasing algorithms in collective variable space. This paper reviews several features and improvements to Colvars that were added since its original introduction. Special attention is given to contributions that significantly expanded the capabilities of this software or its distribution with major MD simulation packages. Collective variables can now be optimized either manually or by machine-learning methods, and the space of descriptors can be explored interactively using the graphical interface included in VMD. Beyond the spatial coordinates of individual molecules, Colvars can now apply biasing forces to mesoscale structures and alchemical degrees of freedom and perform simulations guided by experimental data within ensemble averages or probability distributions. It also features advanced computational schemes to boost the accuracy, robustness, and general applicability of simulation methods, including extended-system and multiple-walker adaptive biasing force, boundary conditions for metadynamics, replica exchange with biasing potentials, and adiabatic bias molecular dynamics. The library is made available directly within the main distributions of the academic software GROMACS, LAMMPS, NAMD, Tinker-HP, and VMD. The robustness of the software and the reliability of the results are ensured through the use of continuous integration with a test suite within the source repository.

De Novo Design of Peptide Binders to Conformationally Diverse Targets with Contrastive Language Modeling.

Designing binders to target undruggable proteins presents a formidable challenge in drug discovery, requiring innovative approaches to overcome the lack of putative binding sites. Recently, generative models have been trained to design binding proteins via three-dimensional structures of target proteins, but as a result, struggle to design binders to disordered or conformationally unstable targets. In this work, we provide a generalizable algorithmic framework to design short, target-binding linear peptides, requiring only the amino acid sequence of the target protein. To do this, we propose a process to generate naturalistic peptide candidates through Gaussian perturbation of the peptidic latent space of the ESM-2 protein language model, and subsequently screen these novel linear sequences for target-selective interaction activity via a CLIP-based contrastive learning architecture. By integrating these generative and discriminative steps, we create a Peptide Prioritization via CLIP (PepPrCLIP) pipeline and validate highly-ranked, target-specific peptides experimentally, both as inhibitory peptides and as fusions to E3 ubiquitin ligase domains, demonstrating functionally potent binding and degradation of conformationally diverse protein targets in vitro. Overall, our design strategy provides a modular toolkit for designing short binding linear peptides to any target protein without the reliance on stable and ordered tertiary structure, enabling generation of programmable modulators to undruggable and disordered proteins such as transcription factors and fusion oncoproteins.

A Modular Cloning Toolkit for the production of recombinant proteins in Leishmania tarentolae.

Modular Cloning (MoClo) is based on libraries of standardized genetic parts that can be directionally assembled via Golden Gate cloning in one-pot reactions into transcription units and multigene constructs. Here, a team of bachelor students established a MoClo toolkit for the protist Leishmania tarentolae in the frame of the international Genetically Engineered Machine (iGEM) competition. Our modular toolkit is based on a domesticated version of a commercial LEXSY expression vector and comprises 34 genetic parts encoding various affinity tags, targeting signals as well as fluorescent and luminescent proteins. We demonstrated the utility of our kit by the successful production of 16 different tagged versions of the receptor binding domain (RBD) of the SARS-CoV-2 spike protein in L. tarentolae liquid cultures. While highest yields of secreted recombinant RBD were obtained for GST-tagged fusion proteins 48 h post induction, C-terminal peptide tags were often degraded and resulted in lower yields of secreted RBD. Fusing secreted RBD to a synthetic O-glycosylation SP20 module resulted in an apparent molecular mass shift around 10 kDa. No disadvantage regarding the production of RBD was detected when the three antibiotics of the LEXSY system were omitted during the 48-h induction phase. Furthermore, the successful purification of secreted RBD from the supernatant of L. tarentolae liquid cultures was demonstrated in pilot experiments. In summary, we established a MoClo toolkit and exemplified its application for the production of recombinant proteins in L. tarentolae.

Chatbots for Change Playbook: A Practical Guide and Modular Toolkit on How to Create, Grow, and Sustain Your Chatbot for Disaster Medicine

Abstract Objective Chatbots, conversational agents providing access to information and services through interaction in everyday language, can make essential health information and services more accessible, available, and affordable. Kati Collective, together with Meta, worked to identify, outline, and develop some of the most critical tools and processes required to leverage chatbots for social good with a focus on health. The results of this work are compiled in a comprehensive, modular playbook. We propose to share our learnings and guide participants of our session to explore the existing uses, benefits, trade-offs, and challenges of using chatbots in disaster medicine. Methods There are significant challenges and barriers to scale, impact, and sustainability of their chatbot interventions. In response to these challenges, we used a participatory design process, landscape analysis, desk research and interviews with key stakeholders in the digital for development space to inform which content to include and in what format to best connect information to action. Results The Playbook contains key information, tools, exercises, and resources organized into four interrelated modules based on barriers and enablers. Conclusions This Playbook is particularly relevant in analysis, planning, strategizing and design phases of the chatbot project lifecycle and is highly extensible to disaster medicine.

Systematic Part Transfer by Extending a Modular Toolkit to Diverse Bacteria.

It is impractical to develop a new parts collection for every potential host organism. It is well-established that gene expression parts, like genes, are qualitatively transferable, but there is little quantitative information defining transferability. Here, we systematically quantified the behavior of a parts set across multiple hosts. To do this, we developed a broad host range (BHR) plasmid system compatible with the large, modular CIDAR parts collection for E.coli, which we named openCIDAR. This enabled testing of a library of DNA constructs across the Pseudomonadota─Escherichia coli, Pseudomonas putida, Cupriavidus necator, and Komagataeibacter nataicola. Part performance was evaluated with a standardized characterization procedure that quantified expression in terms of molecules of equivalent fluorescein (MEFL), an objective unit of measure. The results showed that the CIDAR parts enable graded gene expression across all organisms─meaning that the same parts can be used to program E.coli, P.putida, C.necator, and K.nataicola. Most parts had a similar expression trend across hosts, although each organism had a different average gene expression level. The variability is enough that to achieve the same MEFL in a different organism, a lookup table is required to translate a design from one host to another. To identify truly divergent parts, we applied linear regression to a combinatorial set of promoters and ribosome binding sites, finding that the promoter J23100 behaves very differently in K.nataicola than in the other hosts. Thus, it is now possible to evaluate any CIDAR compatible part in three other hosts of interest, and the diversity of these hosts implies that the collection will also be compatible with many other Proteobacteria (Pseudomonadota). Furthermore, this work defines an approach to generalize modular synthetic biology parts sets beyond a single host, implying that only a few parts sets may be needed to span the tree of life. This will accelerate current efforts to engineer diverse species for environmental, biotechnological, and health applications.

TMA-Mate: An open-source modular toolkit for constructing tissue microarrays of arbitrary layouts

Biomedical research and quality control procedures often demand a variety of microscopic analysis of numerous formalin-fixed and paraffin-embedded (FFPE) tissue samples from different individuals of both healthy and diseased regions of interest. Depending on the number of samples to be analyzed, conventional processing of each FFPE block separately can be laborious or impracticable. This effort can be drastically reduced by using tissue microarrays (TMAs). TMAs have a wide range of applications and can be considered as a high-throughput method to process up to hundreds of miniaturized tissue samples simultaneously on a single microscopy slide, in order to reduce labor, costs and sample consumption, and to increase results comparability. Several commercial and self-made solutions to fabricate TMAs with varying degrees of automation are available. However, these solutions may not be suitable for every situation, either due to high costs, high complexity, lack of precision or lack of flexibility, especially when diagnostically oriented pathology institutes or laboratories with constrained resources are considered. This article introduces the TMA-Mate, an open-source 3D printable modular toolkit for constructing high-density TMAs of arbitrary layouts, providing an affordable, lightweight, and accessible procedure to implement TMAs into existing histology processing pipelines. Step-by-step demonstrations for replicating the hardware and constructing TMAs are included.

CDMAP/CDVIS: context-dependent mutation analysis package and visualization software.

The Context-dependent Mutation Analysis Package and Visualization Software (CDMAP/CDVIS) is an automated, modular toolkit used for the analysis and visualization of context-dependent mutation patterns (site-specific variation in mutation rate from neighboring-nucleotide effects). The CDMAP computes context-dependent mutation rates using a Variant Call File (VCF), Genbank file, and reference genome and can generate high-resolution figures to analyze variation in mutation rate across spatiotemporal scales. This algorithm has been benchmarked against mutation accumulation data but can also be used to calculate context-dependent mutation rates for polymorphism or closely related species as long as the input requirements are met. Output from CDMAP can be integrated into CDVIS, an interactive database for visualizing mutation patterns across multiple taxa simultaneously.

Multiplexed engineering and precision gene editing in cellular immunotherapy.

The advent of cellular immunotherapy in the clinic has entirely redrawn the treatment landscape for a growing number of human cancers. Genetically reprogrammed immune cells, including chimeric antigen receptor (CAR)-modified immune effector cells as well as T cell receptor (TCR) therapy, have demonstrated remarkable responses across different hard-to-treat patient populations. While these novel treatment options have had tremendous success in providing long-term remissions for a considerable fraction of treated patients, a number of challenges remain. Limited in vivo persistence and functional exhaustion of infused immune cells as well as tumor immune escape and on-target off-tumor toxicities are just some examples of the challenges which restrain the potency of today's genetically engineered cell products. Multiple engineering strategies are being explored to tackle these challenges.The advent of multiplexed precision genome editing has in recent years provided a flexible and highly modular toolkit to specifically address some of these challenges by targeted genetic interventions. This class of next-generation cellular therapeutics aims to endow engineered immune cells with enhanced functionality and shield them from immunosuppressive cues arising from intrinsic immune checkpoints as well as the hostile tumor microenvironment (TME). Previous efforts to introduce additional genetic modifications into immune cells have in large parts focused on nuclease-based tools like the CRISPR/Cas9 system or TALEN. However, nuclease-inactive platforms including base and prime editors have recently emerged and promise a potentially safer route to rewriting genetic sequences and introducing large segments of transgenic DNA without inducing double-strand breaks (DSBs). In this review, we discuss how these two exciting and emerging fields-cellular immunotherapy and precision genome editing-have co-evolved to enable a dramatic expansion in the possibilities to engineer personalized anti-cancer treatments. We will lay out how various engineering strategies in addition to nuclease-dependent and nuclease-inactive precision genome editing toolkits are increasingly being applied to overcome today's limitations to build more potent cellular therapeutics. We will reflect on how novel information-rich unbiased discovery approaches are continuously deepening our understanding of fundamental mechanisms governing tumor biology. We will conclude with a perspective of how multiplexed-engineered and gene edited cell products may upend today's treatment paradigms as they evolve into the next generation of more potent cellular immunotherapies.

BreakMi: Reversing, Exploiting and Fixing Xiaomi Fitness Tracking Ecosystem

Xiaomi is the leading company in the fitness tracking industry. Successful attacks on its fitness tracking ecosystem would result in severe consequences, including the loss of sensitive health and personal data. Despite these relevant risks, we know very little about the security mechanisms adopted by Xiaomi. In this work, we uncover them and show that they are insecure. In particular, Xiaomi protects its fitness tracking ecosystem with custom application-layer protocols spoken over insecure Bluetooth Low-Energy (BLE) connections (ignoring standard BLE security mechanisms already supported by their devices) and TLS connections. We identify severe vulnerabilities affecting such proprietary protocols, including unilateral and replayable authentication. Those issues are critical as they affect all Xiaomi trackers released since 2016 and up-to-date Xiaomi companion apps for Android and iOS. We show in practice how to exploit the identified vulnerabilities by presenting six impactful attacks. Four attacks enable to wirelessly impersonate any Xiaomi fitness tracker and companion app, man-in-the-middle (MitM) them, and eavesdrop on their communication. The other two attacks leverage a malicious Android application to remotely eavesdrop on data from a tracker and impersonate a Xiaomi fitness app. Overall, the attacks have a high impact as they can be used to exfiltrate and inject sensitive data from any Xiaomi tracker and compatible app. We propose five practical and low-overhead countermeasures to mitigate the presented vulnerabilities. Moreover, we present breakmi, a modular toolkit that we developed to automate our reverse-engineering process and attacks. breakmi understands Xiaomi application-layer proprietary protocols, reimplements Xiaomi security mechanisms, and automatically performs our attacks. We demonstrate that our toolkit can be generalized by extending it to be compatible with the Fitbit ecosystem. We will open-source breakmi.

The Lightweaver Framework for Nonlocal Thermal Equilibrium Radiative Transfer in Python

Abstract Tools for computing detailed optically thick spectral line profiles out of local thermodynamic equilibrium have always been focused on speed, due to the large computational effort involved. With the Lightweaver framework, we have produced a more flexible, modular toolkit for building custom tools in a high-level language, Python, without sacrificing speed against the current state of the art. The goal of providing a more flexible method for constructing these complex simulations is to decrease the barrier to entry and allow more rapid exploration of the field. In this paper we present an overview of the theory of optically thick nonlocal thermodynamic equilibrium radiative transfer, the numerical methods implemented in Lightweaver including the problems of time-dependent populations and charge-conservation, as well as an overview of the components most users will interact with, to demonstrate their flexibility.

Spatiotemporal Data Set for Out-of-Hospital Cardiac Arrests

We present a spatiotemporal data set of all out-of-hospital sudden cardiac arrests (OHCA) dispatches for the City of Virginia Beach. We also develop a modular toolkit that can be used to process the data and generate problem instances based on user-defined input. The data were collected from multiple sources, and our analysis process was validated by Virginia Beach officials. The data set consists of detailed information about each dispatch made in response to an OHCA; it includes the time the call for service arrived, the response time of the first unit on scene, the address, and the coordinates of each OHCA incident. It also contains detailed spatial information for all existing first-responder stations and both the great-circle and the road distances between all first-responder stations and OHCA incidents. The raw data files were very large in size and were processed using SAS®, MATLAB, and QGIS. In conjunction with the database, we provide a MATLAB code that allows generating multiple random test instances based on user-defined input. The library of problems can be used in healthcare emergency problems and also for facility location models, bilocation problems, and drone studies. The data set was organized such that it can be readily used by researchers in the field of healthcare operations research and those studying the spatiotemporal distribution of OHCAs. Given the difficulty to access OHCA data at the level of detail we provide, the data set will facilitate the implementation of data-driven models to design emergency medical response networks and to study the distribution of OHCAs. Additionally, the provision of data and the toolkit will be very useful in benchmarking algorithms and solvers, which is valuable to the data-driven optimization community in general. Summary of Contribution: The paper provides a data set of spatiotemporal information out-of-hospital cardiac arrests (OHCAs) for the City of Virginia Beach. The complete data set also includes spatial information about all fire, emergency medical services, and police stations in the city and both the road and haversine distances between each pair of stations and OHCA incident. Additionally, we provide a toolkit to generate random instances based on user input. To the best of our knowledge, it is the first time that an OHCA database is made publicly available in such level of detail, and there is no precedent of such in IJOC. OHCAs are a leading cause of death worldwide, and emergency medical services still encounter difficulties in providing care in a timely manner. Given the criticality of OHCAs, we believe that making this data set publicly available can help the implementation of data-driven models by researchers in the field of operations research.

IoT4Fun rapid prototyping tools for Toy User Interfaces

Abstract A Toy User Interface (ToyUI) is a setup that combines smart toy components with other hardware and software components to enhance the player’s experience. We propose a novel Rapid Prototyping Tool to facilitate the development of smart toy components so that creative teams can focus on design improvements. IoT4Fun is a hardware toolkit that supports distributed data collection through wireless communication and contactless identification. It can support a range of play and interface features and limit personal data acquisition by collecting motion tracking information. We compare IoT4Fun single and modular toolkits by embedding them into ToyUI prototypes. The modular toolkit demonstrates more appropriate than the single-board version to design a variety of smart toy components when embedded into different shapes and sizes. A technical assessment compares battery consumption and identifies vulnerabilities of the modular toolkit. Ongoing improvements cover miniaturization, robustness, and designing a secure Application Programming Interface for easier development.

Modularity in making: simplifying solution space for user innovation

An increasingly popular form of open innovation in the digital age is ‘making,’ where users innovate across multiple disciplines and make products that meet their needs, using mechanical, electronic, and digital components. These users have at their disposal, a wide solution space for innovation through various modular toolkits enabled by digital‐age technologies. This study explores and outlines how these users simplify this wide solution space to innovate and make tangible products. Following a modularity theory perspective, it draws on case studies of users and their innovations: (1) Users with initial prototype product designs based on the Internet of things (IoT) from a maker event and (2) users with established product designs from the online community platform Thingiverse. The studies found that users reused the design in the form of existing off‐the‐shelf products and utilized digital fabrication and low‐cost electronics hardware as a ‘glue’ to create physical and informational interfaces wherever needed, enabling bottom‐up modularity. They iteratively refined their innovations, gradually replacing re‐used designs with own integrated designs, reducing modularity, and reducing wastage. The study contributes to open innovation and modularity with implications on the design of products and toolkits enabled by the digital age.

A modular cloning toolkit for genome editing in plants

BackgroundCRISPR/Cas has recently become a widely used genome editing tool in various organisms, including plants. Applying CRISPR/Cas often requires delivering multiple expression units into plant and hence there is a need for a quick and easy cloning procedure. The modular cloning (MoClo), based on the Golden Gate (GG) method, has enabled development of cloning systems with standardised genetic parts, e.g. promoters, coding sequences or terminators, that can be easily interchanged and assembled into expression units, which in their own turn can be further assembled into higher order multigene constructs.ResultsHere we present an expanded cloning toolkit that contains 103 modules encoding a variety of CRISPR/Cas-based nucleases and their corresponding guide RNA backbones. Among other components, the toolkit includes a number of promoters that allow expression of CRISPR/Cas nucleases (or any other coding sequences) and their guide RNAs in monocots and dicots. As part of the toolkit, we present a set of modules that enable quick and facile assembly of tRNA-sgRNA polycistronic units without a PCR step involved. We also demonstrate that our tRNA-sgRNA system is functional in wheat protoplasts.ConclusionsWe believe the presented CRISPR/Cas toolkit is a great resource that will contribute towards wider adoption of the CRISPR/Cas genome editing technology and modular cloning by researchers across the plant science community.

A Toolkit for High Resolution Imaging of Cell Division and Phytohormone Signaling in Legume Roots and Root Nodules.

Legume plants benefit from a nitrogen-fixing symbiosis in association with rhizobia hosted in specialized root nodules. Formation of root nodules is initiated by de novo organogenesis and coordinated infection of these developing lateral root organs by rhizobia. Both bacterial infection and nodule organogenesis involve cell cycle activation and regulation by auxin and cytokinin is tightly integrated in the process. To characterize the hormone dynamics and cell division patterns with cellular resolution during nodulation, sensitive and specific sensors suited for imaging of multicellular tissues are required. Here we report a modular toolkit, optimized in the model legume Lotus japonicus, for use in legume roots and root nodules. This toolkit includes synthetic transcriptional reporters for auxin and cytokinin, auxin accumulation sensors and cell cycle progression markers optimized for fluorescent and bright field microscopy. The developed vectors allow for efficient one-step assembly of multiple units using the GoldenGate cloning system. Applied together with a fluorescence-compatible clearing approach, these reporters improve imaging depth and facilitate fluorescence examination in legume roots. We additionally evaluate the utility of the dynamic gravitropic root response in altering the timing and location of auxin accumulation and nodule emergence. We show that alteration of auxin distribution in roots allows for preferential nodule emergence at the outer side of the bend corresponding to a region of high auxin signaling capacity. The presented tools and procedures open new possibilities for comparative mutant studies and for developing a more comprehensive understanding of legume-rhizobia interactions.

Public Smart Service Provision in Smart Cities: A Case-Study-Based Approach

ABSTRACTIn view of the multitude of challenges urban public administration is struggling with, more and more cities are turning towards smart solutions using information and communication technology. Such Smart City approaches revert to data provided in the urban space by sensors, by citizens or by the archives of the authorities aiming at providing public services that increase the quality of life for citizens, optimize mobility within the city or enhance flexibility and responsiveness of public authorities. This article identifies such SC approaches as well as their basic technical requirements for the cities of Vienna, Singapore, and San Francisco and, by compiling the public service portfolios of these cities, creates an integrative, strategic overview of a SC’s range of services. This approach thus helps to create a scientific basis for the evaluation of SC services and to provide a strategic modular toolkit for public managers to plan and design SCs.

Design and Computational Modeling of a 3D Printed Pneumatic Toolkit for Soft Robotics.

Soft and compliant robotic systems have the potential to interact with humans and complex environments in more sophisticated ways than rigid robots. The majority of the state-of-the art soft robots are fabricated with silicone casting. This method is able to produce robust robotic parts, yet its results are difficult to quantify and replicate. Silicone casting also limits design complexity as well as customization due to the need to make new molds. As a result, most designs are tailored for simple, individual tasks, that is, bending, gripping, and crawling. To address more complex engineering challenges, this work presents soft robots that are fabricated by using multi-material three-dimensional printing. Instead of monolithic designs, we propose a pneumatic modular toolkit consisting of a bending and an extending appendage, as well as rigid building blocks. They are assembled to achieve different tasks. We show that the performance of both appendages is (1) repeatable, that is, the same internal pressure results in the same rotation or extension across multiple specimens and repetitions, and (2) predictable, that is, the respective deformations can be modeled by using finite element analysis. Using multiple instances of both building blocks, we demonstrate the versatility of this toolkit by assembling and actuating a gripper and a crawling caterpillar. The reliability of the mechanics of the building blocks and the assembled robots show that this simple toolkit can serve as a basis for the next generation of soft robots.

HappyTools: A software for high-throughput HPLC data processing and quantitation.

High-performance liquid chromatography (HPLC) is widely used for absolute quantitation. The advent of new columns and HPLC technology has enabled higher sample throughput, and hence, larger scale studies that perform quantitation on different sample types (e.g. healthy controls vs. patients with rheumatoid arthritis) using HPLC are becoming feasible. However, there remains a lack of methods that can analyse the increased number of HPLC samples. To address this in part, the modular toolkit HappyTools has been developed for the high-throughput targeted quantitation of HPLC measurements. HappyTools enables the user to create an automated workflow that includes retention time (tr) calibration, data extraction and the calculation of several quality criteria for data curation. HappyTools has been tested on a biopharmaceutical standard and previously published clinical samples. The results show comparable accuracy between HappyTools, Waters Empower and ThermoFisher Chromeleon. However, HappyTools offered superior precision and throughput when compared with Waters Empower and ThermoFisher Chromeleon. HappyTools is released under the Apache 2.0 license, both the source code and a Windows binary can be freely downloaded from https://github.com/Tarskin/HappyTools.

Optochemical Control of Protein Localization and Activity within Cell-like Compartments.

We report inducible dimerization strategies for controlling protein positioning, enzymatic activity, and organelle assembly inside synthetic cell-like compartments upon photostimulation. Using a photocaged TMP-Haloligand compound, we demonstrate small molecule and light-induced dimerization of DHFR and Haloenzyme to localize proteins to a compartment boundary and reconstitute tripartite sfGFP assembly. Using photocaged rapamycin and fragments of split TEV protease fused to FRB and FKBP, we establish optical triggering of protease activity inside cell-size compartments. We apply light-inducible protease activation to initiate assembly of membraneless organelles, demonstrating the applicability of these tools for characterizing cell biological processes in vitro. This modular toolkit, which affords spatial and temporal control of protein function in a minimal cell-like system, represents a critical step toward the reconstitution of a tunable synthetic cell, built from the bottom up.

A Pulse Voltammetry Analysis Toolkit for Battery and Fuel Cell Materials

Pulse voltammetry (PV) encompasses a range of electroanalytical techniques utilizing a series of voltage pulses to study electrochemical reactions. These techniques provide numerous advantages over other electroanalytical techniques such as cyclic voltammetry (CV) and impedance spectroscopy by providing high sensitivity and discrimination against charging currents. Critically, this enables a more quantitative analysis of the voltammogram than is typical. In conjunction with mathematical models, PV can provide valuable quantitative information on the reaction kinetics, thermodynamics, and consistency with a hypothetical mechanism. Regression of experimental PV data against simulations, as opposed to comparing a limited set of features such as peak heights or full width at half-height, also enables the researcher to evaluate statistical confidence in the model and estimated parameters. The objective of this work is to develop a pulse voltammetry analysis toolkit (PVAT) to automate kinetic parameter estimation, determine confidence, and enable mechanism discrimination using PV experimental data. The desired toolkit will address multiple PV techniques including square wave voltammetry (SWV), one of the most popular pulse techniques, and other common waveforms including normal pulse (NPV) and staircase (SC). As different pulse waveforms are best suited for elucidating different physical phenomena, the ability to analyze several techniques simultaneously provides significant value. Additional objectives include providing statistical analysis and sensitivity to mechanism parameters. Evaluating sensitivity to the PV parameters (e.g. pulse width, pulse height) is also desired, so that the analyst can improve the experimental design to further reduce uncertainty in kinetic, transport, and thermodynamic parameters. A long-term goal is to extend and apply these techniques to porous electrodes, to enable quantitative evaluation of emerging electroactive materials in batteries and fuel cells. This presentation will include examples demonstrating the application of the developed PV toolkit to quantitative analysis of several PV studies using both experimental and simulated data. Support for multiple techniques has been successfully enabled through a modular toolkit design. Applications of the toolkit in simulation mode, which simply outputs the predicted voltammogram for a user specified pulse profile and mechanism, will be presented to verify the toolkit against prior work. The focus will be on the toolkit application in parameter estimation mode, which utilizes experimental data to determine the kinetic and thermodynamic parameters (e.g. rate constants, half-wave potential) for one or more mechanisms which best fit the observed data. Examples of statistical evaluation of multiple mechanisms against voltammogram data will be shown, demonstrating quantitative comparison to determine the most applicable mechanism for a given system. Currently, PVAT can simulate a sequence of any number of sequential single step electron transfer reactions on a planar electrode, with or without homogeneous reactions affecting any of the intermediates. Analysis of multi-step mechanisms including surface adsorption/desorption coupled with surface-confined electron transfer reaction will also be presented. An example application will show PVAT analysis of the oxidation of acetaminophen under acidic, neutral, and basic pH of cyclic using square wave experimental data. Acetaminophen oxidation was selected as a test case because the pH dependence of the mechanism provides a simple means to generate data with several reaction pathways. The estimated parameters were consistent with available literature data and statistical analysis was able to discriminate among the candidate mechanisms and the modeled voltammograms agreed well with experimental data, as shown in Figure 1. The presentation will conclude with preliminary results for analysis of electrochemical reactions in porous electrodes. Development of a computationally efficient software to utilize PV experiments on porous electrodes will enable rapid characterization of novel candidate electrode materials in battery and fuel cell operations more efficiently and confidently than traditional cycling at various rates. The resulting kinetic, thermodynamic, and transport properties will provide critical input for models to accelerate the insertion of promising electrodes into prototype hardware. Ideally, the data obtained from PV experiments on porous electrodes will allow for prediction of battery and fuel cell behavior operating under a range of use conditions. Acknowledgements This material is based upon work supported by the Army Small Business Innovation Research (SBIR) Program Office under Contract Number: W911NF-17-C-0026. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Army Small Business Innovation Research Program Office or the Army Research Office. Figure 1: CSWV Net Voltammograms Comparing Experimental (Markers) and Model (Lines) Using CE-Mechanism of the Oxidation of Acetaminophen at pH1. The Square Wave Frequencies Ranged from 2.5 – 50 Hz. Figure 1