Duration: 10/01/2026 to 09/30/2031

Administrative Advisor(s):

NIFA Reps:

Non-Technical Summary

Biosensors help the in agricultural, food, environmental, and energy industries to detect potentially dangerous contaminants and toxins, and to monitor positive factors such as animal-health biomarkers and soil nutrients. Nanomaterials and advanced data analytics are key to making biosensors effective, but we need to create better ways to integrate them with nanotechnology, and data sciences (including AI and machine learning) to generate solutions for the agriculture, food, environmental, and related industries. That is the main goal of the NC1194 multistate project. We will develop technologies that meet various needs of the above-mentioned industries.

Our work is well-aligned with the current USDA research priorities. Specifically, our research will help 1) increase farmers and ranchers’ profitability through novel ways to boost yields and reduce costs; 2) expand uses of U.S. agricultural products by creating nano-enhanced functional and specialty products; 3) protect the integrity of American agriculture by providing sensitive early detection of invasive pests and diseases and by verifying food safety, quality, and traceability; 4) promote soil health by developing real-time soil-monitoring capabilities; and 5) improve human health, for instance, by supporting breeding strategies for nutrient-dense crops and by enabling data-driven labeling.

The member institutions will closely collaborate on research, education, and outreach activities that will advance the relevant technological fields, train the future workforce, and promote commercialization and industry adoption of the technologies and solutions that we develop. Our ultimate goal is to enhance the productivity and economic efficiency of U.S. agriculture to better serve our national interests and the American public over the next five years and beyond.

Statement of Issues and Justification

The development of sensing and processing technologies enabled by nanotechnology and nanomaterials, powered by artificial intelligence (AI), machine learning (ML), and data science, to serve the agricultural, food, environmental, and energy sectors, remains the central goal of the NC-1194 multistate project. Even though great progress has been made in the scientific understanding of nanoscale phenomena and molecular mechanisms of interactions between nanomaterials and biological systems, much remains to be explored. For example, how can nanotechnology and nanomaterials be effectively utilized to address the need for rapid and reliable detection and mitigation of pathogens that impact agricultural and food systems? How do artificial/synthesized nanomaterials impact the environment and ecosystems? How can nanotechnology be used to improve food security? How can the vast quantity of data collected via nano-enabled biosensors be used to support AI-enabled decision-making? These are just several examples among many that call for further investigation and exploration at the crossroads of biosensors, nanotechnology, AI, and data science that impact agricultural, food, environmental, and other biological systems.

Our proposed work aligns well with the new priorities set by the Secretary of Agriculture’s memorandum No. 0253.25. Specifically, our research on biosensors and nanotechnology will help increase profitability of farms and ranches, expand markets for new uses of U.S. agricultural products, protect the integrity of American agriculture from invasive species, promote soil health, and improve human health through precision nutrition and food quality.   

Related, Current and Previous Work

Overview

The NC1194 multistate project started in October 2006. Over the last 20 years, the multistate committee has aimed to provide a suite of distributed technologies based on nanomaterials and biosensors to monitor the natural world in real time, to analyze aggregated data in the context of high biological and climatological complexity,  to adaptively address threats, and to take advantage of opportunities for improving agricultural and food safety, quality, and security. Biosensor and nanotechnology research is evolving rapidly. A review conducted by the NC1194 committee members in 2021 showed that the number of publications in the biosensor area has been doubling each decade, with some 5,000 peer-reviewed papers are being published per year (McLamore et al., 2021). Over the period that this project has been active, most biosensor manuscripts  emerged from institutions located in China, followed by the United States and India, with just 3–5% of the total publications coming from South Korea, Iran, Germany, the United Kingdom, Japan, Italy, Spain, Canada, and France. Medical applications accounted for approximately 10% of all biosensor publications, followed by food (5%), environmental sciences (3%), and agriculture (2%) (McLamore et al., 2021). Agriculture-relevant areas have long been a top focus of the biosensor-research community.

A recent report concluded that seven major foodborne pathogens accounted for more than  53,000 hospitalizations and at least 931 deaths in 2019 alone (Scallan Walter et al., 2025). Most devices developed by biosensor researchers have been for the detection of foodborne pathogens, followed by pesticides, GMO foods, food toxins, food allergens, and chemical residues in food (Griesche and Baeumner, 2020). In addition, supply-chain issues caused by mold, pests, and poor handling, among other causes, destroy 31% of all the food produced in the U.S. (Buzby et al., 2014). Reducing such food losses also calls for effective monitoring by easy-to-use biosensors.

A common theme across the broad spectrum of sensing-technology research is the need to understand complex molecular, cellular and biological processes. Great strides have been made in biosensor research over the past two decades, but much remains to be explored. As of five years ago, most biosensor manuscripts were addressing decades-old questions and promising only incremental improvement based on previously successful strategies (McLamore et al., 2021). Our analysis suggests that this is still the case, and that innovations are still  strongly needed in four key areas. These are 1) integrated sample preparation, 2) label-free or direct detection in complex matrices, 3) long-term sensor stability, and 4) lower-cost and more user-friendly devices. Members of the NC1194 project aim achieve breakthroughs in all four of these key areas over the next five years.

To make biosensors more powerful, and to improve agricultural products and foods in general, nanomaterials are increasingly utilized as molecular and cellular probes, and as additives to improve functionalities and properties of biological materials, foods and feeds. Hence, systematic study of nanoscale processes/materials and the development of nano-enabled biosensing apparatus are critical (Ramesh et al., 2022). Nanotechnological advances have paved the way for using various nanomaterials to contact and directly interact with specific target biomolecules or analytes. As compared to their traditional counterparts, the biosensors that incorporate such materials can be much more sensitive, specific, and fast-acting, thanks to a range of customized magnetic, electrical, chemical and optical properties that enable various signal transducers (Mao et al., 2018; Zhu et al., 2018). Hence, nanotechnology-enhanced biosensors can be used in different bioengineering applications such as drug-delivery applications. Integration of nanotechnology into biosensors for agriculture, food, and environmental surveillance also confers advantages such as large surface-to-volume ratio, manifestation of biological transduction and signaling mechanisms, and various readout mechanisms (e.g., McLamore et al., 2021;  Rizwan et al., 2022);  and it has been demonstrated that using nanomaterials/nanostructures for biosensing applications can increase another important sensor property, limit of detection (LOD) (Ramesh et al., 2022). Cutting-edge nano-enabled biosensors have been shown to rapidly detect the target analyte, exhibit single-molecule level sensitivity, and considerably boost throughput as compared to non-nano-enabled ones (Ramesh et al., 2022).

The obstacles to biosensors’ widespread use have eased quite substantially. However, the world will not be able to unlock nanotechnology’s incredible potential to improve biosensors unless three key issues are resolved (D’Souza et al., 2017; Kailasa et al., 2020; McLamore et al., 2021). The first is the presently unavoidable emission of nanoparticles into the environment and the atmosphere (Aspermair et al., 2021; Simon et al., 2019). The second is that quantum effects produce exceptionally high sensitivity, random noise, and background signals, which can lead to certain analytes being cross-sensitive, nonlinear, and unpredictable. The third is that materials such as graphene, which are promising for biosensing applications, have not been effectively mass-produced.  The members of the NC1194 project will explore ways to overcome each of them on the way to making better nano-enabled biosensors that can address the needs of U.S. agriculture. 

In addition, biosensors and nanotechnologies that provide decision support to farmers, food, processors, and consumers are increasingly taking advantage of remote networking and AI. Building upon our prior achievements, we will take full advantage of the new capacities of AI-powered data analytics in this area developing new tools that will endow biosensors with higher specificity and sensitivity, better response times, higher throughput, and better reliability.

This multistate project provides a collaborative framework in which researchers within and beyond the North Central region of the U.S. will share their expertise in multiple disciplines including agricultural and biological engineering, chemical engineering, toxicology, plant and soil sciences, food science, nanotechnology, electrochemistry, analytical chemistry, and microbiology. Collaborations over the past five years (i.e., the previous NC1194 project) have yielded research grants, publications, and devices. Research outcomes have also been disseminated at conferences and as university courses. In the past five years, members of the NC-1194 group collectively published 300+ refereed papers, many  in the top journals in the field including Biosensors and Bioelectronics. Most of these publications were authored or co-authored by the more than 40 graduate students who have been trained in the participants’ laboratories. Project members made at least 80 presentations, participated in and organized workshops, taught relevant courses, managed two NSF-funded REU programs and participated in the USDA REEU program to train next-generation scientists and engineers. In addition, the project has been regularly highlighted by the Multistate Research Fund Impacts Program and covered in the scientific press (i.e., NSF Research News and CEP Magazine) (AZ). The members collaborated on review papers on antimicrobial resistance, biosensors in food, agriculture, and the environment, and micro/nano-plastics in agricultural and food systems (McLamore et al., 2022; Yu et al., 2023). It is also worth noting that our members have made several research tools for identifying relevant research publicly available (SENSEE; SC) and shared protocols for research related to biosensors and antimicrobial resistance (IA, MI, SC). The results were disseminated to the industry and scientific communities at the annual meetings of professional societies, the Global Alliance for Rapid Diagnostics (GARD) symposium, and other scientific conferences including Gordon Research Conferences (GRC), and those of the American Society of Agricultural and Biological Engineering (ASABE), Institute of Food Technologists (IFT), American Chemical Society (ACS), Society of Environmental Toxicology and Chemistry (SETAC), and International Association for Food Protection (IAFP).

A search of the NIMSS database revealed no duplication of the next proposed phase of our project. Specifically, although there have been five past projects on nanotechnology and biosensors  (NCDC 201 10/01/2004-2006, NC1031 10/1/2006-09/30/2011; NC 1194, 10/1/2011-9/30/2016; 10/1/2016-9/30/2021; 10/1/2021-9/30/2026), our project extends all of them in critical respects.

Not conducting the proposed work could have several unfortunate consequences. In particular, the development and deployment of advanced biosensors for real-time soil-health monitoring, as well as innovations in precision nutrition through nanoencapsulation and nano-packaging, may be realized at a slower pace, or not at all. In addition, the absence of this research could limit opportunities to enhance the efficiency and sustainability of agricultural production systems and to fully leverage emerging nanotechnology-enabled tools for improving crop productivity and resource-use efficiency. This, in turn, could reduce the potential for U.S. agriculture to maintain its leadership in adopting and translating cutting-edge technologies into practical applications that benefit farmers and consumers. Finally, the project provides a critically important platform for training the next generation of interdisciplinary researchers at the intersection of nanotechnology, agriculture, and food systems. Without it, opportunities for developing advanced technical expertise and preparing a highly skilled workforce to address future agricultural challenges may be damaged. Research impact of past work

In the past, the NC1194 group has produced numerous advances in nanomaterials-enabled biosensor technologies across a spectrum of applications for improving the safety and security of agriculture and food systems as well as maintaining quality across the food supply chain. Among these notable advances are 1) innovative nanomaterial morphologies, such as coupling cellulose nanofiber (CNF) to Surface Enhanced Raman Scattering (SERS) for the rapid detection of paraquat residues in vegetables (MO); 2) assessment of metabolite distributions in plant and animal cells (IA, UT); 3) development of strategies to stabilize enzymes for the fabrication of glucose and alcohol biosensors with long operational life (GA); and 4) enhancement of the performance of a variety of other biosensing modalities for rapid detection of pathogens, prions, viruses, contaminants (e.g., heavy metals), pesticides, and antibiotics residues in water and agricultural and food products (AR, AZ, FL, HI, IA, MI, WI).

Beyond detection, we have developed nanotechnologies for the removal of contaminants. These have included nanocomposite membranes for the removal and degradation of the persistent, toxic polyfluoroalkyl substances in drinking water (PFAS, KY), nanomaterials for the removal of heavy metals (WI), nanomaterials to reduce microbials and chemical contaminants in irrigation water (CT), and nanofibers to capture chemicals and pathogens from liquids (NY).

These breakthroughs have all been made possible by our research on the sustainable development of nanotechnology via molecular-level understanding of the interaction of nanomaterials with biological interfaces. More specifically, such research underpins both the design of applications that interface with biological systems and the evaluation of the potential risks posed by the release of nanoscale materials into the environment. Several groups (KY, WI and SC) have made significant progress in characterizing and understanding the environmental and health risks and toxicity mechanisms of a wide array of commonly used nanomaterials in food, agricultural, and biological systems, both before and after environmental transformations in simple and complex exposure scenarios. Our members’ deep multidisciplinary collaborations have addressed antimicrobial resistance, advancing affordable technologies to enhance the speed and sensitivity of the recovery and detection of pathogenic bacteria (AR, MI) as well as the rapid clinical identification of antimicrobial resistance in pathogenic bacteria (MI). We have also developed novel approaches in which  functionalized nanomaterials succeeded in both preventing and treating animal models’ infections with pathogenic organisms(SC); engineered new nanomaterials for targeted drug and vaccine delivery (AR, IA); and developed vaccines against animal viruses: e.g., one for swine disease, in which a chimeric purified protein based on constructed Hepatitis B core antigen is self-assembled into virus-like particles (VA). Other innovations from the group include edible films with novel antimicrobial and nutritional properties(NJ), nanomaterials to improve efficiency rates for value-added bioprocessing (AR, WI) and functional foods (IA). On the methodological side, we developed electrochemical analyses of biomolecule interactions at nanoscale, such as DNA-chain hybridization and antibody-antigen interactions, that rely on impedance spectroscopy and differential-pulse voltammetry (SD).

Significant progress has been made in the development, characterization, and optimization of self-assembled nanobrush/aptamer hybrid nanostructures for sensing (SC), enhancing the electrocatalytic performance of nickel oxide nanoparticles on three-dimensional carbonized eggshell membrane for the detection of urea in alkaline solution (SD), and DNA biosensors (SC, IA, UT, HI). These DNA biosensors’ construction process includes multiple ‘self-assembly’ steps that allow their surfaces to form highly specific DNA structures, which have been able to recognize target DNA sequences specific to Cryptosporidium in water samples (UT) and SARS-CoV-2 virus in saliva (SC, IA, HI).

The project team’s use of smartphones for the rapid, economic, and user-friendly analysis of biosensing signals has been further improved, including via the incorporation of sophisticated image-processing algorithms and coupling with a fluorescent microscope for the detection of pathogens and environmental toxicants (AZ, HI). We have also been working on the use of fluorescence detection to improve assay reliability and reproducibility, and successfully demonstrated that it can be used to detect cancer markers in blood as well as for nucleic-acid amplification. A conceptually new sensing mechanism for E. coli and Zika virus utilizing capillary flow, has also been developed (AZ). A user-friendly prototype for analyzing field water for norovirus and aerosols and human saliva samples for SARS-CoV-2 has been designed, fabricated, and tested for sensitivity and specificity (AZ).

Researchers in FL and SC developed value-added nanotechnology products from agricultural waste for food packaging, solar cells, and sensors, as well as new sensor systems for studying signaling in plant/mammalian systems. Submicron fibers with surface properties able to capture and concentrate target chemicals, pathogens (E. coli) and biological molecules in microfluidic devices were also fabricated (NY).

Complementing the team’s work on discrete biosensing technologies for wide distribution in the environment, several participants in this project have been working on networking approaches and AI that can aggregate data and provide meaningful decision support in the spheres of  food quality, safety, and the cost-effectiveness of agriculture and bioprocessing (AR, AZ, FL, SC, MI, NJ). A novel Raspberry Pi-based optical device along with a support vector machine (SVM)-based ML algorithm for classifying oil types from oil-spill samples in seawater has been developed, fabricated, and tested, achieving  > 90% accuracy (AZ).

Several members of NC1194 pooled their expertise to collaboratively assess the state of biosensor research on the North American continent, as a means of  identifying critical challenges and opportunities, and guiding future research. This effort produced a refereed review of food, environmental and agricultural sensing technologies (FEAST). In 2022, prompted by the emerging threat to the environment and public health posed by micro- and nano-plastics, the committee published another comprehensive review, this time of bioanalytical approaches to the detection, characterization, and risk assessment of micro- and nano-plastics in agriculture and food systems. This paper was the first attempt in the U.S. by food and agricultural researchers to identify the key knowledge and technology gaps, and provided a crucial roadmap for research in this field. The collaborative impact of the committee to the relevant research community is well represented by these contributions.

Over the next five years, the members of the NC1194 group plan to continue working on better biosensors; using nanotechnology to address a range of problems in the U.S. agriculture, food and healthcare industries; and engaging in active collaborations among all member institutions to achieve the goals laid out in this proposal.

Education and extension impact

Project members are highly active in instruction, developing and sharing teaching/training materials related to nanotechnology and biosensors. This includes leading two NSF-REU programs (IA) where students contributed to research and development on wearable graphene-based stress sensors. The Project participants also conducted workshops with and disseminated training manuals to high school teachers in the U.S. (FL, MD) and abroad (Colombia, China), with the aim of enabling them to teach their students to create flexible graphene circuits (FL).

 

In the past five years, led by MI participants, the GARD Forum held global technical sessions, short courses, and an Innovation Challenge, covering nanotechnology and biosensors, among other topics. The forum hosted almost 1,500 participants from 38 different countries. The MI team demonstrated nanoparticles and biosensors at the Science Festival of the state of Michigan, which is designed for all age groups. Together with Extension specialists from the SmartPath Center of Excellence, SC members have developed a series of workshops, videos, and open-source protocols that are currently being disseminated through extension programs in FL, IA, MD (UMES), as well as other partner institutions. These workshops and extension materials are focused on how to use electrochemical biosensors for pathogen detection during fresh-produce production (in irrigation water, on frost-protection materials, etc.). SC participants have developed three diagnostic kits and established a fabrication protocol for manufacturing at regional scale. Participants from HI, GA, and AR have led the Biosensor Committee ITSC 230 of the American Society of Agricultural and Biological Engineering (ASABE), organizing and chairing oral sessions at the annual international meeting. Beyond the borders of the U.S., members from NY organized short courses on nano-biotechnology and nano and biosensors as well as full courses on advanced topics in colloidal science and nanoscale delivery systems for biologically active molecules, that were taught in Kazakhstan and the Philippines.

 

Various initiatives saw groups partner to strengthen this project’s ties to industry, including by identifying and executing new research needs, and to boost global capacity to research and develop practical, cost-effective biosensing technologies. In partnership with GARD, we are also establishing centers of excellence (COEs) around the world, and agreements with food companies to validate and license technologies for rapid pathogen extraction and detection (MI). We aim to build upon our past successes, and promote more collaborations with relevant industries, to bring about commercialization and adoption of biosensors and nanotechnologies by various stakeholders and thus bring a range of benefits to the U.S. agriculture, food and healthcare industries and to the American public.

Objectives

1. Enable nanotechnology-based smart sensors for accurate, reliable, cost-effective, onsite, and rapid detection of various entities relevant to agricultural, plant, and animal production, food supply chain, and environmental systems
   
2. Support the sustainable development of nano-based products and technologies for agricultural production, post-harvest processing, and packaging of nutritious and healthful food.
   
3. Assess environmental and health implications of engineered nanomaterials in agricultural, terrestrial, and aquatic systems.
   
4. Integrate AI and ML tools with nano/biosensors and other nanoscale systems derived data for effective decision support for agricultural and food security, safety, and sustainability.
   
5. Develop instructional modules to educate and prepare the future workforce on nanofabrication, biosensing, AI and ML tools, and nanomaterials-posed risks and engage with industry partners to commercialize the developed technologies.
   

Methods

In the next five years, the members of the NC1194 group will continue working collaboratively to advance the five objectives listed above. We will develop various methodologies and tools to achieve all the goals of the project. Below is a summary of methods we will use.

Objective 1: Development of biosensors

In working toward this goal, we will develop biosensors enabled by nanotechnology that can provide accurate, reliable, cost effective, onsite and rapid detection of a broad range of target entities that are relevant to agriculture, food and environmental systems. The group members will utilize many different physical, chemical and biological means to create biosensors that can meet those various needs. The signal transduction methods we will explore include electrochemical, optical/photonic, gravitational, acoustic, spectroscopic, and thermal ones, among others. We will create different types of nanomaterials and nanostructures, including metallic nanoparticles and structures, bendable and wearable graphene substrates, carbon nanotubes, inorganic and organic nanoparticles, etc., and develop strategies to form nanomaterials-bioconjugate composites that can serve as nanoprobes, thus enhancing the performance of biosensors. The group members will also explore various types of biosensor setups, including lab-in-a-tube, microfluidic microchip, etc., to facilitate easy deployment of the biosensors, including in the field; and develop smartphone-based readouts and data analytics that will support high throughput and fast data generation and interpretation. An example of a smartphone-based microfluidic biosensor developed by Dr. Yoon (AZ) to detect micro- and nano-plastics in water is shown in Fig.1, which also shows how data analytics will be incorporated with the biosensor. In a nutshell, we aim to develop methods and tools that can be easily adopted and used by everyday users to serve our stakeholders. All participating institutions expect to contribute to this effort.

https://nimss.org/storage/11818/NC1194-fig.1.jpg

Fig.1. An example of a smartphone-based microfluidic biosensor platform for detecting micro- and nano-plastics in water

The key to the success of the NC1194 project is active collaboration among all participants facilitated by the technical committee. Because no single researcher or laboratory owns all the instruments that are needed for biosensor research, sharing existing resources at the participating institutions will greatly boost the research of each individual member. In addition, cross-validation is critically important in biosensor development to assess the effectiveness of any method, which requires active collaboration within the community that will be greatly aided by the technical community, as we have demonstrated before. Via the NC1194 committee email list, members have maintained active communications in the past, shared ideas, samples and data; and provided important feedback to one another. We will keep doing so in the future: pooling data on key collaborative projects to boost data analysis, and generating multistate summarization of discoveries that can increase their impacts. We envision that the collaboration achieved through the NC1194 project will be  a key factor in adding value to our individual efforts in our pursuit of research goals.

 

Objective 2. Utilization of nanomaterials

In pursuit of our second objective, we will explore ways of utilizing nanomaterials to enhance agricultural and food products to better serve consumers. We will create nanocarriers (e.g., hollow metallic/silicon nanoparticles, natural polymer nanoparticles, etc.) that can facilitate transport and delivery of nutrients, therapeutics, drugs and vaccines to animals and humans with improved efficacy. We will also generate nano-enhanced micronutrient packs (e.g., Zn, Se, and Fe nanoparticles) that can improve plant nutrient uptake to boost crop yields. We will create food-packaging materials with embedded nanosensors that allow time-tracking and continuous monitoring of foods to assure their safety and to reduce food waste; and create nanomaterials such as catalytic TiO₂ nanoparticles that can be incorporated into post-harvest processing to improve food safety and security. We will also explore nano-enhanced micronutrients and functional materials as food additives to produce more nutritious and functional food to serve consumers with special needs (e.g., microfiber enhanced texture-modified food for dysphagia patients). We will utilize various methods of nanofabrication, ranging from wet-chemistry synthesis, to laser lithography, to produce nanomaterials with desirable properties, and develop effective ways to incorporate them into products, foods and processes. We will also develop means of monitoring for nanomaterials’ presence and for interactions between them and biological systems. This will allow us to assess the safety implications of their presence in products and processes, and to ensure that their utilization will not  elevate safety risks to consumers. As described under Objective 1, collaboration among NC1194 members using the multistate committee will be a key platform for our efforts to achieve these goals.. All participating institutions expect to contribute to this effort.

Objective 3. Assessment of impacts of nanomaterials

In this context, we will use a number of model systems ranging from nematodes such as C. elegans to animals such as mice and rats to study how artificial nanomaterials may interact with biological systems, and to assess and evaluate the potential risks associated with  such materials. We will also use various characterization methods – including but not limited to spectroscopic microscopy, electron microscopy, atomic force microscopy, and magnetic resonance imaging – to study the interactions between nanomaterials and biological molecules, with the aim of understanding the mechanisms whereby the former may subvert the proper functioning of biological systems. We will design and implement rigorous procedures for sampling and evaluating the impacts of engineered nanomaterials that enter agricultural and environmental systems, and utilize the biosensors developed under Objective 1 to collect data in the real world. Again, collaboration among all NC1194 participants is critical to achieving these goals. Biosensors developed by some members will be used by others to conduct data collection, and the data will be shared across all groups  to build proper data analytics and find patterns that may reveal underlying mechanisms of universal significance. We will build a cloud-based data hub to which all members can upload environmental data, which will enable a deeper and more comprehensive understanding of the behavior and risk implications of both engineered and natural nanomaterials. All participating institutions expect to contribute to this effort.

Objective 4. Data sciences and AI-enabled decision support

As rapid progress in AI changes the landscape of technology development and scientific discovery, in the next five years the members of the NC1194 project will focus on harvesting AI’s power to further our understanding of nanomaterials and nanoscale phenomena, and to develop better biosensors with increasing abilities to meet the needs of our stakeholders in various industries. For example, Dr. Reyes-De-Corcuera (UF) is using novel protein-folding algorithms such as OpenFold and AlphaFold3 to predict folding of enzyme variants produced in silico as well as enzyme sequences whose 3D structures have not been characterized empirically. This approach is accelerating the discovery of suitable enzymes for biosensor fabrication and applications. Dr. Yu (IA) is using a large-scale protein embedding (Roche et al., 2024) with E(3)-equivariant graph neural networks (EGNNs) called EquiPNAS-ESM2 (Lin et al., 2023) to study the characteristics of binding between proteins and nanoplastics. As shown in Fig.2, this approach is yielding information likely to lead to better-designed sensor platforms for nanoplastic detection and mitigation, as well as a better understanding of  how nanoplastics’ sizes, shapes, and chemical properties, along with environmental factors, could impact their interactions with and risks to biosystems. Such knowledge was not obtainable in the past by purely experimental means, i.e., without AI.

https://nimss.org/storage/11819/NC1194-fig.2.jpg

Fig. 2 An example of AI predicted binding patterns between different nanoplastics and proteins

AI algorithms’ effectiveness depends on the size of their training datasets: more data and better training will further improve their power. However, no one group could generate the scale of data needed to meaningfully improve the performance of AI tools for the purposes of this project. Therefore, all groups will contribute to training-data collection and other aspects of AI training, which in turn will lead to better and more reliable AI and data tools that will benefit all. More specifically, this will involve developing a publicly-accessible cloud-based data-sharing mechanism whereby all members of the project can upload their sharable data, and exploring methods to use those shared data to train ML/AI models to meet our various needs. The NC1194 project is the platform that will make it possible for us to pursue these goals. All participating institutions will contribute to this effort.

Objective 5. Educational and industrial engagement

In the past, it has always been a critical goal of the NC1194 multistate committee to develop instructional modules to educate and prepare the future workforce for nanofabrication, nanomaterials- risk assessment/mitigation, and biosensing. For the next five years, we also aim to add AI and ML tools to our educational efforts as we all believe that our future workforce needs to be well versed in the latest advances in those areas. The members aim to jointly develop instructional modules for educating students at our institutions about the relevant topics via a “backward” design strategy that starts by defining the learning objectives, before moving to the creation of content and assessments that will ensure those objectives are met. We plan to utilize the following specific methodologies for the development of instructional modules:

• Needs Assessment: Surveying industry partners and academic programs to identify key skills gaps in the workforce.
• Module Creation: Developing a mix of theoretical content (lectures, readings) and hands-on activities (virtual labs, detailed protocols for sensor fabrication).
• Assessment Design: Creating pre- and post-module quizzes to measure knowledge gain and rubrics for grading hands-on tasks to measure skills acquisition.

We also plan to implement experiential learning, i.e., integrate the teaching modules into active learning environments. We will design and implement course-based undergraduate research experiences (CUREs), and embed research projects into existing lab courses, allowing undergraduates to actively engage in and contribute to research activities. We will support graduate students through our research efforts, training them to become pillars of next-generation research in relevant fields. We will also develop opportunities for summer Internships/REU programs to host students from other institutions to and thereby broaden the project’s reach. All of these actions will rely on active collaboration among all members and member institutions.

Last but not least, all members will actively engage with industry partners and stakeholders to push for commercialization and adoption of the developed technologies. Over the next five years, we will work collaboratively to seek funding to host symposia and/or workshops that bring industrial partners and academicians together to build stronger ties and to boost academic-industry collaborations.   

Measurement of Progress and Results

Outputs

• The project will deliver a range of tangible outputs aligned with its five core objectives. For Objective 1, the focus will be on developing prototype nano-enabled smart sensors for multiple agricultural applications, accompanied by validation reports that demonstrate their accuracy, reliability, and cost-effectiveness, as well as comprehensive technical documentation and performance datasets. Objective 2 will produce nano-enabled packaging prototypes, establish guidelines for sustainable nano-product development, and generate life-cycle assessment reports to ensure environmental responsibility. Under Objective 3, the team will create risk-assessment models and compile toxicity data, publish peer-reviewed studies on the environmental fate and transport of engineered nanomaterials, and develop regulatory compliance guidelines to inform safe practices. For Objective 4, outputs will include AI and ML algorithms designed for sensor-data interpretation, interactive decision-support dashboards for stakeholders, and robust data integration frameworks to enhance agricultural decision-making. Finally, Objective 5 will deliver online curricula and training modules, form industry-academia partnership agreements, and create certification programs to prepare a skilled workforce and accelerate technology commercialization.

Outcomes or Projected Impacts

• The success of this project will advance the application of nanotechnology and AI in agriculture and food systems to enhance productivity, sustainability, and safety. Achievement of its first objective will yield prototype nano-enabled smart sensors for multiple agricultural applications, supported by validation reports on accuracy, reliability, and cost-effectiveness, along with comprehensive technical documentation and performance datasets. These innovations, enabling rapid, on-site detection of critical entities, can be expected to boost adoption of advanced sensing technologies, improve decision-making about disease prevention and food safety, and reduce losses across production and supply chains. Ultimately, this will strengthen food security, lower economic losses from contamination and outbreaks, and enhance America’s global competitiveness in agri-tech. Achievement of the project’s second objective will result in nano-enabled packaging prototypes, guidelines for sustainable nano-product design, and life-cycle assessment reports. This will reduce spoilage, extend shelf life, and increase consumer confidence in nano-enabled packaging while minimizing its environmental impacts. Its broader positive impacts can be expected to include improved nutrition and food quality, progress toward a circular economy in food, and stronger industry standards for sustainable nanotechnology. Attainment of our third objective will yield much clearer knowledge of the environmental and health implications of engineered nanomaterials in agricultural, terrestrial, and aquatic systems. By creating risk-assessment models, and regulatory-compliance guidelines, and by conducting toxicity studies, we will improve understanding of nanomaterials’ interactions with ecosystems and inform best practices for their safe use. These outcomes will reduce environmental and health risks, build public trust in nanotechnology applications, and support sustainable integration of nanomaterials into agriculture. When this project’s fourth objective is achieved, effective decision-support solutions for agricultural and food security will have been provided via integration of AI and ML tools with data derived from nano/biosensors and other nanoscale systems. Deliverables will include AI/ML algorithms for sensor-data interpretation, decision-support dashboards, and data-integration frameworks. These tools will enable faster and more accurate predictions for crop health and food safety, enhance precision-agriculture practices, and reduce resource waste. This, in turn, can be expected to increase resilience in food systems, improve agricultural productivity and sustainability, and reinforce the U.S.’s position as a global leader in agri-tech innovation. Attainment of our fifth and final objective will yield meaningful advances in both workforce development and industry engagement. Instructional modules will be created to educate future professionals in nanofabrication, biosensing, AI/ML tools, and nanomaterials risk assessment. Partnerships with industry will facilitate the commercialization of the developed technologies and provide experiential-learning opportunities through certification programs. These efforts can also be expected to accelerate technology transfer, ultimately strengthening the innovation ecosystem and driving economic growth. Together, these objectives form a comprehensive strategy to leverage nanotechnology and AI for sustainable agricultural production, improved food safety, and enhanced global food security, while addressing environmental and health considerations and preparing the next generation of innovators. All participating institutions expect to contribute to these efforts.

Milestones

(2027):The project will be implemented over five years (2027-2031), with clearly defined milestones for each objective to ensure systematic progress and measurable outcomes. Across all project objectives, the first year will focus on establishing a foundational framework for meeting them. In the case of Objective 1, this year will be dedicated to sensor design and materials selection, which will lay the foundations for nano-enabled smart sensors. For Objective 2, it will involve identifying target applications and defining sustainability metrics, followed by the drafting of guidelines for nano-product development. Objective 3’s first year will include data collection on nanomaterials’ behavior in agricultural, terrestrial, and aquatic systems. For Objective 4, the first year will focus on developing a robust data-collection and preprocessing pipeline for sensor outputs. Finally, Objective 5’s initial year will focus on the design of a curriculum framework covering nanofabrication, biosensing, AI/ML tools, and risk assessment.

(2028):In Objective 1’s second year, prototypes will undergo rigorous validation for accuracy, reliability, and cost-effectiveness under controlled conditions. The same year will also see the creation of Objective 2’s nano-enabled packaging prototypes and their safety and performance evaluations, as well as toxicity and exposure studies and the building of preliminary risk models relevant to Objective 3. The AI and ML algorithms required for Objective 4’s predictive analytics will be trained and validated, and the instructional modules and certification programs relevant to Objective 5 will be piloted.

(2029):By the third year, as the teams work toward Objective 1, field trials will be conducted to benchmark performance, and comprehensive technical documentation and datasets will be compiled. On the way to Objective 2, lifecycle-assessment reports will be completed, and pilot-scale production will begin. Objective 3’s third year will focus on model validation, publication of the relevant findings, and sharing of certain findings and data with regulatory bodies. Objective 4’s third year will involve deploying decision-support dashboards on pilot farms and refining them based on user feedback; and in Objective 5’s third year, formal industry partnerships will be established, and internships and collaborative projects will commence.

(2030):Across all objectives, the foci of activities in the fourth year will be expanded validation, pilot deployment, and partnership-building. More specifically, in the case of Objective 1, field trials will begin assessing sensor performance under practical conditions, while technical documentation and datasets will continue to be compiled. Objective 2’s fourth year will involve completing lifecycle-assessment reports and pilot-scale production efforts. In year four of Objective 3, risk models will undergo further validation, the findings from which will be prepared for dissemination to regulatory bodies and other stakeholders. Objective 4’s fourth year will focus on expanding and refining the decision-support dashboards by gathering user feedback, and enhancing their functionality and usability to better support decision-making. For Objective 5, fourth-year efforts will focus on ensuring that the quality of industry engagement, internships, and collaborative projects remain strong.

(2031):Objective 1’s fifth year will concentrate on optimizing the developed sensors and engaging with industry partners to facilitate their commercialization. On the way to Objective 2, the final year’s focus will be on preparing a commercialization roadmap and strategies for industry adoption. Objective 3’s final-year activities will culminate in the establishment of best-practice guidelines and their integration into policy frameworks to ensure the safe and sustainable use of nanomaterials. Objective 4’s fifth year will involve scaling up for integration into regional agricultural systems, and publishing performance reports to demonstrate impact. Finally, the fifth year of the teams’ work toward Objective 5 will include expanding training programs nationally, tracking workforce placement, and documenting commercialization outcomes, thereby ensuring a strong pipeline of skilled professionals and industry-ready technologies. Together, these milestones provide a structured roadmap for achieving the project’s objectives, ensuring the timely delivery of outputs, outcomes, and impacts that advance agricultural sustainability, food security, and technological innovation.

Projected Participation

View Participation Form/Appendix E: Participation

Outreach Plan

This outreach initiative aims to raise awareness, foster innovation, and build capacity around the transformative role of nanotechnology and biosensors in agriculture, food systems, and One Health. The plan is to engage an audience that includes industry stakeholders, extension agents, researchers, students, and the general public through dynamic, accessible, and interactive formats. Alongside the use of routine academic pathways (e.g., peer-reviewed journals, professional conferences, etc.) to disseminate the findings made by all participants, we will collaborate in the following outreach efforts.

• Infographics on nanotechnology in One Health: Visually engaging infographics will illustrate how nano-enabled biosensors can contribute to the interconnected health of humans, animals, plants, water, and the environment. These materials will be distributed at events and online to simplify complex concepts for broad audiences.
• Short educational videos: A series of concise, animated videos will explain the societal impact of nano-biosensors, highlighting their applications in food safety, disease detection, environmental monitoring, and sustainable agriculture. These will be shared via social media, YouTube, and educational platforms.
• Short courses on nanotechnology and biosensors: Modular online courses will be developed to introduce learners to the fundamentals of nanotechnology and biosensing, and to their applications in agriculture and food systems. These courses will target students, extension professionals, and early-career researchers.
• Student Innovation Challenge: A competitive event will invite students to design and prototype nanotechnology-enabled biosensor solutions addressing real-world agricultural and food system challenges, encouraging interdisciplinary collaboration and entrepreneurial thinking. Winners will be recognized at conferences.

Platforms for Engagement will include:

• GARD Forum: A venue for presenting research, sharing outreach materials, and networking with global experts in diagnostics and biosensing.
• Science festivals: Public-facing events where interactive exhibits, infographics, and videos will engage families, educators, and community members in the science of nanotechnology and biosensors.
• Undergraduate research conferences and forums: Opportunities for students to present their work, participate in the Innovation Challenge, and explore careers in nano-agriculture.
• Agri-tech and stakeholder conferences: Industry-focused events where outreach materials and short courses will be shared with agricultural professionals, industry stakeholders, policymakers, and technology developers.

Organization/Governance

The USDA-Multistate NC-1194 project “Nanotechnology and Biosensors” must be supported by a governance structure that ensures strategic alignment, interdisciplinary collaboration, ethical oversight, and effective translation of research into practice. Below is the governance framework tailored to the mission of NC1194.

1. USDA National Program Manager, assigned by USDA

Purpose: Provide strategic direction, ensure alignment with the USDA mission, and oversee high-level decisions.

Responsibilities:

• Approve strategic plans and funding allocations
• Review annual progress and impact reports
• Ensure integration with USDA priorities and national needs

2. Scientific Advisor, selected by USDA

Purpose: Guide scientific rigor, innovation, and interdisciplinary integration.

Responsibilities:

• Evaluate research proposals and progress
• Recommend emerging technologies and methodologies
• Advise on data standards and interoperability

3. NC1194 Officers – Chair, Vice-Chair, and Secretary

Purpose: Provide coordination, strengthen collaboration among members, and promote outreach activities

Rule of Succession: The secretary is the only person elected by members during the annual meeting. The secretary slides into the Vice-Chair position the following year, then becomes the Chair in two years.

Responsibilities:

• Organize annual meetings
• Compile annual station reports, including summarizing outcomes, innovations, and community benefits
• Monitor outreach plan implementation
• Engage industry stakeholders and communities

Literature Cited

Aspermair P., Mishyn V., Bintinger J., Happy H., Bagga K., Subramanian P., Knoll W., Boukherroub R., Szunerits S. (2021). Reduced graphene oxide–based field effect transistors for the detection of E7 protein of human papillomavirus in saliva. Anal. Bioanal. Chem. 413:779–787. doi: 10.1007/s00216-020-02879-z.

Buzby, J. C., Wells, H. F., Hyman, J., (2014). The Estimated Amount, Value, and  Calories of Postharvest Food Losses  at the Retail and Consumer Levels  in the United States, USDA ERS report, www.ers.usda. gov/publications/eibeconomic-informationbulletin/EIB-121.aspx

D’Souza A.A., Kumari D., Banerjee R. (2017). Nanobiosensors. Academic Press; Cambridge, MA, USA: 2017. Nanocomposite biosensors for point-of-care—Evaluation of food quality and safety; pp. 629–676.

Griesche, C., Baeumner, A.J., (2020). Biosensors to support sustainable agriculture and food safety. TrAC Trends Anal. Chem., 128, 115906, 10.1016/j.trac.2020.115906

Kailasa S., Rani B.G., Reddy M.S.B., Jayarambabu N., Munindra P., Sharma S., Rao K.V. (2020). NiO nanoparticles-decorated conductive polyaniline nanosheets for amperometric glucose biosensor. Mater. Chem. Phys. 242:122524. doi: 10.1016/j.matchemphys.2019.122524.

Lin, Z., Akin, H., Rao, R. M., Hie B., Zhu Z., Lu W., Smetanin N., Verkuil R., Kabeli O., Shmeuli Y., Costa A., Fazel-Zarandi M., Sercu T., Candido S., Rives A., (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science. 379(6637), 1123–1130

Mao W., He H., Sun P., Ye Z., Huang J. (2018). Three-dimensional porous nickel frameworks anchored with cross-linked Ni(OH)2 nanosheets as a highly sensitive nonenzymatic glucose sensor. ACS Appl. Mater. Interfaces. 10:15088–15095. doi: 10.1021/acsami.8b03433.

McLamore, E. S., Alocilja, E., Gomes, C., Gunasekaran, S., et al., (2021). FEAST of biosensors: Food, environmental and agricultural sensing technologies (FEAST) in North America, Biosensors and Bioelectronics, 178, 113011. https://doi.org/10.1016/j.bios.2021.113011

Scallan Walter, E. J., Cui, Z., Tierney, R., Griffin, P. M., et al., (2025). Foodborne Illness Acquired in the United States—Major Pathogens, 2019. Emerging Infectious Diseases, 31(4), 669-677. https://doi.org/10.3201/eid3104.240913.

Ramesh, M., Janani, R., Deepa, C., Rajeshkumar, L., (2022). Nanotechnology-Enabled Biosensors: A Review of Fundamentals, Design Principles, Materials, and Applications. Biosensors 13(1):40. doi: 10.3390/bios13010040.

Roche R., Moussad B., Md Hossain, S., Tarafder S., Bhattacharya D. (2024). EquiPNAS: improved protein-nucleic acid binding site prediction using protein-language-model-informed equivariant deep graph neural networks. Nucleic Acids Res. 52(3), e27

Rizwan K., Rahdar A., Bilal M., Iqbal H.M. (2022). MXene-based electrochemical and biosensing platforms to detect toxic elements and pesticides pollutants from environmental matrices. Chemosphere. 291:132820. doi: 10.1016/j.chemosphere.2021.132820

Simon J., Flahaut E., Golzio M. (2019). Overview of Carbon Nanotubes for Biomedical Applications. Materials. 12:624. doi: 10.3390/ma12040624.

Zhu X., Ju Y., Chen J., Liu D., Liu H. (2018). Nonenzymatic wearable sensor for electrochemical analysis of perspiration glucose. ACS Sens. 3:1135–1141. doi: 10.1021/acssensors.8b00168

Attachments

Land Grant Participating States/Institutions

AZ, IA, IL, IN, KS, KY, MI, MN, MO, MS, NC, NY, VA, WI

Non Land Grant Participating States/Institutions