[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119661-en":3,"doc-seo-119661-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119661,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Machine-learning-guided design of electroanalytical pulse waveforms","Voltammetry supports detection and quantification of electroactive species, yet designing effective pulse waveforms is difficult because the search space is combinatorially large and design principles are limited. Serotonin illustrates the challenge of in situ measurement under low concentrations and interferents with similar structures. This work introduces rapid-pulse voltammetry waveform optimization using Bayesian optimization via a machine-learning workflow (SeroOpt) that improves over random and human-guided designs. The approach supports tunable selective analyte detection and yields interpretable domain-aligned design logic.","Open Access Article . Pu on 10 June 2025. Down on 1/14/2026blished loaded 11:12:00 AM .  \nDigital  \nDiscovery  \nPAPER  \nView Article Online View Journal | View Issue  \nCite this: Digital Discovery, 2025, 4, 1812  \nReceived 6th January 2025 Accepted 4th June 2025  \nDOI: 10.1039/d5dd00005j[rsc.li/digitaldiscovery](rsc.li/digitaldiscovery)  \nMachine-learning-guided design of electroanalytical pulse waveforms†  \nCameron S. Movassaghi,  ‡§*ab Katie A. Perrotta,  ‡a Maya E. Curry,c  \nAudrey N. Nashner,a Katherine K. Nguyen,a Mila E. Wesely,de Miguel Alcañiz Fillol,f Chong Liu,  a Aaron S. Meyerg and Anne M. Andrews  *abgh  \nVoltammetry is widely used to detect and quantify oxidizable or reducible species in complex environments. The neurotransmitter serotonin epitomizes an analyte that is challenging to detect in situ due to its low concentrations and the co-existence of similarly structured analytes and interferents. We developed rapid-pulse voltammetry for brain neurotransmitter monitoring due to the high information content elicited from voltage pulses. Generally, the design of voltammetry waveforms remains challenging due to prohibitively large combinatorial search spaces and a lack of design principles. Here, we illustrate how Bayesian optimization can be used to hone searches for optimized rapid pulse waveforms. Our machine-learning-guided workflow (SeroOpt) outperformed random and human-guided waveform designs and is tunable a priori to enable selective analyte detection. We interpreted the black box optimizer and found that the logic of machine-learning-guided waveform design reflected domain knowledge. Our approach is straightforward and generalizable for all single and multi-analyte problems requiring optimized electrochemical waveform solutions. Overall, SeroOpt enables data-driven exploration of the waveform design space and a new paradigm in electroanalytical method development.  \nIntroduction  \nVoltammetry is widely employed across 􀀁elds, including energy storage,1 catalysis,2 organic synthesis,3 and electroanalysis (i.e., neuroscience,4–8 diagnostics,9 environmental applications,10 and food and beverage analysis11) . Despite the many types of  \naDepartment of Chemistry & Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA. E-mail: [aandrews@mednet.ucla.edu](aandrews@mednet.ucla.edu); [csmova@g.ucla.edu](csmova@g.ucla.edu)bCalifornia NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA  \ncInstitute of Society and Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA  \ndDepartment of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA 90095, USA  \neDepartment of Psychology, University of California, Los Angeles, Los Angeles, CA 90095, USA  \nfInteruniversity Research Institute for Molecular Recognition and Technological Development, Universitat Politcnica de Valncia -Universitat de Valncia, Camino de Vera s/n, Valencia, 46022, Spain  \ngDepartment of Bioengineering, University of California, Los Angeles, Los Angeles, CA 90095, USA  \nhDepartment of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, and Hatos Center for Neuropharmacology, University of California, Los Angeles, Los Angeles, CA 90095, USA  \n† Electronic supplementary information (ESI) available. See DOI:  \n[https://doi.org/10.1039/d5dd00005j](https://doi.org/10.1039/d5dd00005j)  \n‡ These authors contributed equally to this work.  \n§ Present address: Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048 .  \nanalytes suitable for voltammetry, few design principles exist to enable analyte-speci􀀁c voltammetry waveforms to be identi􀀁ed and optimized systematically. This lack of objectively guided waveform design and optimization imposes signi􀀁cant limitations on the accuracy, selectivity, and robustness of voltammetry applications for single- or multi-analyte detectio","cbCaie2w0oJ71eD5","https://ap.wps.com/l/cbCaie2w0oJ71eD5","pdf",2808263,1,21,"English","en",105,"# Introduction\n## Voltammetry and waveform design challenges\n## Relevance to neurotransmitter monitoring\n## Existing waveform development approaches","[{\"question\":\"Why is designing voltammetry pulse waveforms challenging?\",\"answer\":\"Waveform parameters are hard to optimize systematically due to prohibitively large combinatorial search spaces and a lack of general design principles.\"},{\"question\":\"How does SeroOpt improve rapid pulse waveform design?\",\"answer\":\"SeroOpt uses Bayesian optimization to guide the search for optimized pulse waveforms, outperforming random and human-guided designs.\"},{\"question\":\"Can the machine-learning workflow be adapted for different analytes?\",\"answer\":\"Yes. SeroOpt is tunable a priori to enable selective analyte detection for single- and multi-analyte electrochemical problems.\"}]","Machine-learning-guided design of electroanalytical pulse waveforms | PDF",1785725541,53,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-guided-design-of-electroanalytical-pulse-waveforms","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-guided-design-of-electroanalytical-pulse-waveforms/119661/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is designing voltammetry pulse waveforms challenging?","Question",{"text":75,"@type":76},"Waveform parameters are hard to optimize systematically due to prohibitively large combinatorial search spaces and a lack of general design principles.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does SeroOpt improve rapid pulse waveform design?",{"text":80,"@type":76},"SeroOpt uses Bayesian optimization to guide the search for optimized pulse waveforms, outperforming random and human-guided designs.",{"name":82,"@type":73,"acceptedAnswer":83},"Can the machine-learning workflow be adapted for different analytes?",{"text":84,"@type":76},"Yes. 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