Patent Application
20250229247
Colloidal nanoparticles have garnered significant interest due to their potential applications in areas such as catalysis, photonics, optics, electrochemistry, and energy storage. nanoscale catalysts, with their high density of active sites like surface steps, kinks, and edges, play a role in enhancing catalytic performance. The dedicated design and fabrication of these catalysts can aid in fulfilling the increasing demand of renewable and sustainable energy technologies.
Despite extensive research, the discovery and refinement of advanced nanoparticles present formidable challenges, stemming mainly from the intricate, multi-step chemistry of their synthesis. This process demands a delicate balance of various precursors and reaction conditions, including temperature and time, across a vast synthetic space. For instance, examining ten different settings for each of six parameters (four chemicals, temperature, and time) results in one million potential combinations. Traditional trial-and-error methods for identifying optimal synthesis parameters are time-consuming, labor-intensive, and inefficient, given the vast of the parameter space. Thus, there is a need for methods and systems for producing colloidal nanoparticles that is relatively efficient and adaptable to different experimental settings. These needs and other needs are satisfied by the present disclosure.
In accordance with the purpose(s) of the disclosure, as embodied and broadly described herein, the disclosure, in one aspect, relates to a system for nanoparticle synthesis, comprising: a self-driven fluidics platform and a reactor module configured to control environmental conditions during synthesis of a nanoparticle. The self-driven fluidics platform comprises: a chemical handling module comprising: a plurality of chemical reservoirs, each chemical reservoir configured to hold a fluid; and a mixer, comprising a plurality of injector ports and at least one ejector port, each chemical reservoir in fluidic communication with at least one injector port of the mixer, the mixer configured to mix at least two fluids entering the mixer from the injector ports, thereby forming an initial mixture, and deliver the initial mixture through the ejector port as part of a segmented flow. The reactor module comprises: a flow reactor in fluidic communication with the mixer through the ejector port, the flow reactor comprising a channel configured to allow the segmented flow to move through the flow reactor via the channel, the flow reactor comprising at least one observation window configured to enable real-time characterization of nanoparticles in individual droplets in the segmented flow through the flow reactor.
Also disclosed herein is a method a method comprising: mixing together at least two fluids, thereby forming a segmented flow comprising a plurality of droplets; flowing the segmented flow into a reactor; controlling a temperature of the reactor; measuring at least one target property of nanoparticles in individual droplets in the segmented flow as the plurality of droplets pass through the reactor; and adjusting formation of droplets added to the segmented flow based upon the measured at least one target property of the nanoparticles.
Other systems, methods, features, and advantages of the present disclosure will be or become apparent to one with skill in the art upon examination of the following drawings and detailed description. It is intended that all such additional systems, methods, features, and advantages be included within this description, be within the scope of the present disclosure, and be protected by the accompanying claims. In addition, all optional and preferred features and modifications of the described aspects are usable in all aspects of the disclosure taught herein. Furthermore, the individual features of the dependent claims, as well as all optional and preferred features and modifications of the described aspects are combinable and interchangeable with one another.
Many aspects of the present disclosure can be better understood with reference to the following drawings. The components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Moreover, in the drawings, like reference numerals designate corresponding parts throughout the several views.
Additional advantages of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or can be learned by practice of the disclosure. The advantages of the disclosure will be realized and attained by means of the elements and combinations particularly pointed out in the appended claims. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure, as claimed
Many modifications and other aspects disclosed herein will come to mind to one skilled in the art to which the disclosed compositions and methods pertain having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is to be understood that the disclosures are not to be limited to the specific aspects disclosed and that modifications and other aspects are intended to be included within the scope of the appended claims. The skilled artisan will recognize many variants and adaptations of the aspects described herein. These variants and adaptations are intended to be included in the teachings of this disclosure and to be encompassed by the claims herein.
Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.
As will be apparent to those of skill in the art upon reading this disclosure, each of the individual aspects described and illustrated herein has discrete components and features which may be readily separated from or combined with the features of any of the other several aspects without departing from the scope or spirit of the present disclosure.
Any recited method can be carried out in the order of events recited or in any other order that is logically possible. That is, unless otherwise expressly stated, it is in no way intended that any method or aspect set forth herein be construed as requiring that its steps be performed in a specific order. Accordingly, where a method claim does not specifically state in the claims or descriptions that the steps are to be limited to a specific order, it is no way intended that an order be inferred, in any respect. This holds for any possible non-express basis for interpretation, including matters of logic with respect to arrangement of steps or operational flow, plain meaning derived from grammatical organization or punctuation, or the number or type of aspects described in the specification.
All publications mentioned herein are incorporated herein by reference to disclose and describe the methods and/or materials in connection with which the publications are cited. The publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the present disclosure is not entitled to antedate such publication by virtue of prior disclosure. Further, the dates of publication provided herein can be different from the actual publication dates, which can require independent confirmation.
While aspects of the present disclosure can be described and claimed in a particular statutory class, such as the system statutory class, this is for convenience only and one of skill in the art will understand that each aspect of the present disclosure can be described and claimed in any statutory class.
It is also to be understood that the terminology used herein is for the purpose of describing particular aspects only and is not intended to be limiting. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the disclosed compositions and methods belong. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the specification and relevant art and should not be interpreted in an idealized or overly formal sense unless expressly defined herein.
Prior to describing the various aspects of the present disclosure, the following definitions are provided and should be used unless otherwise indicated. Additional terms may be defined elsewhere in the present disclosure.
As used herein, “comprising” is to be interpreted as specifying the presence of the stated features, integers, steps, or components as referred to, but does not preclude the presence or addition of one or more features, integers, steps, or components, or groups thereof. Moreover, each of the terms “by”, “comprising,” “comprises”, “comprised of,” “including,” “includes,” “included,” “involving,” “involves,” “involved,” and “such as” are used in their open, non-limiting sense and may be used interchangeably. Further, the term “comprising” is intended to include examples and aspects encompassed by the terms “consisting essentially of” and “consisting of.” Similarly, the term “consisting essentially of” is intended to include examples encompassed by the term “consisting of.
As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items. Expressions such as “at least one of,” when preceding a list of elements, modify the entire list of elements and do not modify the individual elements of the list.
As used herein, nomenclature for compounds, including organic compounds, can be given using common names, IUPAC, IUBMB, or CAS recommendations for nomenclature. When one or more stereochemical features are present, Cahn-Ingold-Prelog rules for stereochemistry can be employed to designate stereochemical priority, E/Z specification, and the like. One of skill in the art can readily ascertain the structure of a compound if given a name, either by systemic reduction of the compound structure using naming conventions, or by commercially available software, such as CHEMDRAW™ (Cambridgesoft Corporation, U.S.A.).
Reference to “a” chemical compound refers to one or more molecules of the chemical compound rather than being limited to a single molecule of the chemical compound. Furthermore, the one or more molecules may or may not be identical, so long as they fall under the category of the chemical compound. Thus, for example, “a” chemical compound is interpreted to include one or more molecules of the chemical, where the molecules may or may not be identical (e.g., different isotopic ratios, enantiomers, and the like).
As used in the specification and the appended claims, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a nanoparticle,” “a fluid,” or “a droplet,” includes, but is not limited to, two or more such nanoparticles, fluids, or droplets, and the like.
Reference to “a/an” chemical compound each refers to one or more molecules of the chemical compound rather than being limited to a single molecule of the chemical compound. Furthermore, the one or more molecules may or may not be identical, so long as they fall under the category of the chemical compound.
It should be noted that ratios, concentrations, amounts, and other numerical data may be expressed herein in a range format. It is to be understood that such a range format is used for convenience and brevity, and thus, should be interpreted in a flexible manner to include not only the numerical values explicitly recited as the limits of the range, but also to include all the individual numerical values or sub-ranges encompassed within that range as if each numerical value and sub-range is explicitly recited. To illustrate, a concentration range of “about 0.1% to about 5%” should be interpreted to include not only the explicitly recited concentration of about 0.1 wt % to about 5 wt %, but also include individual concentrations (e.g., 1%, 2%, 3%, and 4%) and the sub-ranges (e.g., 0.5%, 1.1%, 2.2%, 3.3%, and 4.4%) within the indicated range. The term “about” can include traditional rounding according to significant figures of numerical values. In addition, the phrase “about ‘x’ to ‘y’” includes “about ‘x’ to about ‘y’”
When a range is expressed, a further aspect includes from the one particular value and/or to the other particular value. For example, where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the disclosure, e.g. the phrase “x to y” includes the range from ‘x’ to ‘y’ as well as the range greater than ‘x’ and less than ‘y’. The range can also be expressed as an upper limit, e.g. ‘about x, y, z, or less’ and should be interpreted to include the specific ranges of ‘about x’, ‘about y’, and ‘about z’ as well as the ranges of ‘less than x’, less than y′, and ‘less than z’. Likewise, the phrase ‘about x, y, z, or greater’ should be interpreted to include the specific ranges of ‘about x’, ‘about y’, and ‘about z’ as well as the ranges of ‘greater than x’, greater than y′, and ‘greater than z’. In addition, the phrase “about ‘x’ to ‘y’”, where ‘x’ and ‘y’ are numerical values, includes “about ‘x’ to about ‘y’”.
It is to be understood that such a range format is used for convenience and brevity, and thus, should be interpreted in a flexible manner to include not only the numerical values explicitly recited as the limits of the range, but also to include all the individual numerical values or sub-ranges encompassed within that range as if each numerical value and sub-range is explicitly recited. To illustrate, a numerical range of “about 0.1% to 5%” should be interpreted to include not only the explicitly recited values of about 0.1% to about 5%, but also include individual values (e.g., about 1%, about 2%, about 3%, and about 4%) and the sub-ranges (e.g., about 0.5% to about 1.1%; about 5% to about 2.4%; about 0.5% to about 3.2%, and about 0.5% to about 4.4%, and other possible sub-ranges) within the indicated range.
As used herein, the terms “about,” “approximate,” “at or about,” and “substantially” mean that the amount or value in question can be the exact value or a value that provides equivalent results or effects as recited in the claims or taught herein. That is, it is understood that amounts, sizes, formulations, parameters, and other quantities and characteristics are not and need not be exact, but may be approximate and/or larger or smaller, as desired, reflecting tolerances, conversion factors, rounding off, measurement error and the like, and other factors known to those of skill in the art such that equivalent results or effects are obtained. In some circumstances, the value that provides equivalent results or effects cannot be reasonably determined. In such cases, it is generally understood, as used herein, that “about” and “at or about” mean the nominal value indicated ±10% variation unless otherwise indicated or inferred. In general, an amount, size, formulation, parameter or other quantity or characteristic is “about,” “approximate,” or “at or about” whether or not expressly stated to be such. It is understood that where “about,” “approximate,” or “at or about” is used before a quantitative value, the parameter also includes the specific quantitative value itself, unless specifically stated otherwise.
As used herein, the terms “optional” or “optionally” means that the subsequently described event or circumstance can or cannot occur, and that the description includes instances where said event or circumstance occurs and instances where it does not.
Unless otherwise specified, temperatures referred to herein are based on atmospheric pressure (i.e. one atmosphere).
A self-driven fluidics platform is disclosed herein that integrates automation, inline analytics, and, optionally, AI-driven active learning. This system is designed for the autonomous synthesis of target nanomaterials, efficiently navigating the complex parameter space to achieve the desired properties, significantly streamlining the nanoparticle production process. The advanced approach in material synthesis revolves around an automated, high-throughput, and mobile droplet fluidics platform engineered for diverse applications in wet chemistry synthesis. The system can comprise a combination of four hardware modules: the chemical handling module, the reactor module, the in-line characterization module, and/or a control module, each module optionally mounted on a compact rack for easy mobility and efficiency. The nanoparticles produced by any system or method disclosed herein can have a variety of sizes and shapes. Nanoparticle shapes include, but are not limited to, spheres, cubes, rods, wires, polygonal plates, amorphous, polyhedrons, stars, shells, and the like.
More specifically, in one aspect, disclosed herein is a system for nanoparticle synthesis, comprising a self-driven fluidics platform comprising a chemical handling module and a reactor module configured to control environmental conditions during synthesis of a nanoparticle. In a further aspect, the chemical handling module can comprise a plurality of chemical reservoirs, each chemical reservoir configured to hold a fluid; and a mixer, comprising a plurality of injector ports and at least one ejector port, each chemical reservoir in fluidic communication with at least one injector port of the mixer, the mixer configured to mix at least two fluids entering the mixer from the injector ports, thereby forming an initial mixture, and deliver the initial mixture through the ejector port as part of a segmented flow. In another further aspect, the reactor module can comprise a flow reactor in fluidic communication with the mixer through the ejector port, the flow reactor comprising a channel configured to allow the segmented flow to move through the flow reactor via the channel, the flow reactor comprising at least one observation window configured to enable real-time characterization of nanoparticles in individual droplets in the segmented flow through the flow reactor. In one aspect, the chemical reservoirs can each comprise a syringe pump. In another aspect, at least one of the chemical reservoirs can comprise an oil. The oil can be a carrier oil, such as a silicone oil. In another aspect, the mixer can be a static mixer, such as a helical static mixer. In another aspect, the flow reactor can be further configured to control environmental conditions of the channel, such as a temperature of the channel. The system can also further comprise a filtration device configured to prevent the formation of bubbles within the fluids of the system.
This chemical handling module can manage the flow of chemicals with the system. It can include at least one of an on demand chemical supply reservoirs, syringe pumps, a compressed gas generator, multi-channel gas pressure control pumps, flow sensors, a bubble-removing device, three-way liquid switches, or multi-channel rotary valves. The inclusion of a bubble-removing device can ensure a pure chemical flow by eliminating unwanted bubbles. The rotary valve device can facilitate sequential injection of reagents, effectively preventing cross-contamination and ensuring precise flow control at the sub-microliter level.
The reactor module module can include a custom-designed reactor (referred to as a Fluidics Synthesis Reactor (FSR)) comprising two aluminum plates enclosing six-turn zig-zag shaped glass tubing. The FSR can include heating rods and a resistance temperature detector (RTD), supporting temperatures exceeding 300° C. The FSR can be equipped with strategically placed holes (e.g., 54) for in-situ measurement probes, accommodating both optical and x-ray observations. Hydrophobic modification of the glass tubing's internal wall can allow for forming aqueous droplets, and positioning the reactor on a motorized X-Z stage can allow for computer-controlled alignment with optical or x-ray paths for in-situ observation.
The system can further comprise an in-line characterization module comprising an analytical instrument optionally configured to obtain characterization data of the nanoparticles in the individual droplets in the segmented flow while the segmented flow moves through the channel of the flow reactor. In one aspect, the analytical instrument can comprise a radiation emitter and a signal detector. In another aspect, the analytical instrument can be configured to deliver radiation through the at least one observation window and detect a radiation signal emitted from at least one individual droplet. Examples of radiation emitters include an X-ray light source (e.g., X-ray tubes, rotating anodes, or synchrotrons), UV-Vis-IR light source (e.g., deuterium arc lamp, tungsten-halogen lamp, or xenon flash lamp), laser source (e.g., low noise, visible wavelength with different power levels), and LED diodes. Examples of signal detectors include X-ray detectors, UV-Vis-IR detectors, avalanche photodiodes (APDs) and optical camera and microscopy. Various characterization methods can be integrated into the system for real-time analysis, including dynamic light scattering utilizing a laser source, avalanche photodiodes, and digital autocorrelators; optical imaging and microscopy, and solution property analyzers for measuring viscosity and electrical conductivity.
In one aspect, the in-line characterization module can include a UV-Vis light source and a spectrometer capable of collecting real-time spectral data from 350 to 800 nm. The mobile design of the system permits adaptability for high-throughput SAXS/WAXS measurements at various synchrotron beamlines, allowing detailed characterization needed for complex nanomaterial synthesis.
The system can further comprise a control module comprising processing circuitry configured to at least: receive an analyte measurement from the analytical instrument; and at least one of: adjust a rate of delivery of the fluid of at least one chemical reservoir; or adjust a volume of delivery of the fluid of at least one chemical reservoir. In a further aspect, the control module can dynamically adjust operation of the chemical handling module or the reactor module during synthesis of the nanoparticle. In a further aspect, the dynamic adjustment can be in response to machine learning analysis of the real-time characterization data. The system can allow for characterization of multiple analyte parameters, including: nanoparticle dimensional analysis, including particle size measurements and particle size distribution determinations; optical properties, including ultraviolet-visible absorption spectra and photoluminescence characteristics; colloidal stability parameters, including dispersity status; and bulk solution properties, including dynamic viscosity and electrical conductivity measurements.
The control module can comprise an eight-channel power switch device and a 48-channel network hub, connecting to a central computer for overarching control. This setup can ensure seamless integration and control of any electronic devices, cameras, and motor systems. In another aspect, the control module can be configured for remote access. A verified user can securely log into the system from remote locations, allowing a user to conduct experiments and monitor processes remotely. This can enhance the platform's flexibility and accessibility, making it a valuable tool for collaborative and distant research endeavors.
As an example, a typical process of high-throughput synthesis can begin with the delivery of each chemical reagent through PTFE tubing to the bubble-removing device. Subsequently, reagents can be sequentially injected via the rotary valve into the reactor, where they form droplets in a carrier phase and flow through a static mixer into the reactor's glass tubing. Here, chemical reactions take place and are subject to in-line characterization, with the products being collected at the outlet.
Continuing with the example, to achieve autonomous synthesis, the platform can employ innovative droplet recognition algorithms, including data correlation and neural network techniques, for accurate real-time identification of each droplet. These algorithms support effective data analysis and autonomous synthesis. Machine learning integration, especially active algorithms based on Gaussian processes, guides the synthesis and predicts chemical recipes and conditions. In a typical autonomous synthesis, the system can initially produce around 20 droplets with random recipes. Utilizing real-time characterization and data analysis, the Gaussian process algorithm develops a predictive model to suggest new recipes. This iterative process continues until the desired material properties are achieved. As explored in the Examples section below, a representative system can produce nanoparticles with narrow polydispersity across a comprehensive range of synthetic parameters, with high efficiency in self-driven autonomous synthesis capabilities at a synchrotron beamline. By employing active learning, the system can efficiently produced gold nanoparticles with minimized size distribution, selecting from a vast array of possible recipe combinations.
The disclosed system introduces a transformative approach to nanoparticle synthesis. The system can comprise can advanced, automated, mobile, and self-driven fluidics platform, specifically tailored for high-throughput autonomous nano-synthesis using wet-chemistry methods. This platform overcomes the inefficiencies typically associated with traditional trial-and-error methods in nanoparticle synthesis. The methodology can operate on several principles: precision and automation in Chemical Handling, a custom-designed droplet reactor, real-time in-line characterization tools and data analysis techniques, and machine-learning integration.
Precision and Automation in Chemical Handling: The system can employ syringe pumps, multi-channel pumps with gas control, flow sensors, and bubble-removing modules to ensure precise handling of chemical reagents. This accuracy facilitates exploration of the vast synthetic space of nanoparticle synthesis, where the correct combination of precursors, reductants, solvents, and other chemicals is needed for successful synthesis.
Custom-Designed Droplet Reactor. The reactor can be engineered to facilitate reactions under precisely controlled temperatures, creating isolated environments within each droplet for chemical reactions. The design can include hydrophobic, optical, and x-ray transparent glass tubing, along with strategically positioned holes for in-situ measurements. The motorized stage can enhance the reactor's functionality by enabling facile and precise monitoring over the reaction process.
Real-Time In-line Characterization Tools and Data Analysis Techniques: Integration of UV-Vis spectroscopy and optical microscopes permits immediate analysis of the ongoing synthesis process. This immediate feedback allows for rapid identification of successful synthesis conditions and the ability to make prompt parameter adjustments. The adaptability for high-throughput SAXS/WAXS measurements at various synchrotron beamlines further elevates the platform's material characterization capabilities, which is important for complex nanomaterial synthesis. The innovative droplet recognition algorithms enable real-time identification of each droplet, which is utilized for downstream data analysis and autonomous synthesis.
Machine Learning Integration: The optional incorporation of machine learning, such as Gaussian process algorithms, empowers the system to dynamically predict and optimize chemical recipes and reaction conditions. This capability can significantly streamline the process of identifying the most promising reaction conditions within a vast synthetic space.
In practical applications, the system demonstrates remarkable efficiency and high-throughput capabilities. For instance, in a laboratory setting focused on gold nanoparticle synthesis, the system conducted over 3000 experiments in about 100 hours, while using only approximately 120 ml of chemical solutions. To put this in perspective, a chemistry-trained Ph.D. student conducting 10 samples per day would need approximately 300 business days to achieve a similar output. This exemplifies the platform's efficiency in resource utilization and experimentation. By leveraging active learning and real-time analysis, the system significantly reduces the number of experimental iterations needed. As shown in the Examples, the system successfully minimized the size distribution of nanoparticles by autonomously selecting from about 19,730 possible recipe combinations, using only around 150 reactions in under 10 hours. The ability to analyze reactions in real-time and adjust parameters accordingly greatly enhances the responsiveness and efficiency of the synthesis process, key factors in rapidly achieving the desired nanoparticle properties.
Features of the system can include, but are not limited to, the following:
The proposed system offers many technical and economic advantages including, but not limited to:
The proposed system can be utilized in a wide range of research and industrial applications including, e.g.:
The following listing of exemplary aspects supports and is supported by the disclosure provided herein.
Aspect 1. A system for nanoparticle synthesis, comprising: a self-driven fluidics platform comprising: a chemical handling module comprising: a plurality of chemical reservoirs, each chemical reservoir configured to hold a fluid; and a mixer, comprising a plurality of injector ports and at least one ejector port, each chemical reservoir in fluidic communication with at least one injector port of the mixer, the mixer configured to mix at least two fluids entering the mixer from the injector ports, thereby forming an initial mixture, and deliver the initial mixture through the ejector port as part of a segmented flow; and a reactor module configured to control environmental conditions during synthesis of a nanoparticle, comprising: a flow reactor in fluidic communication with the mixer through the ejector port, the flow reactor comprising a channel configured to allow the segmented flow to move through the flow reactor via the channel, the flow reactor comprising at least one observation window configured to enable real-time characterization of nanoparticles in individual droplets in the segmented flow through the flow reactor.
Aspect 2. The system of aspect 1, further comprising an in-line characterization module comprising an analytical instrument configured to obtain characterization data of the nanoparticles in the individual droplets in the segmented flow while the segmented flow moves through the channel of the flow reactor.
Aspect 3. The system of aspect 2, wherein the analytical instrument comprises a radiation emitter and a signal detector.
Aspect 4. The system of aspect 2 or aspect 3, wherein the analytical instrument is configured to deliver radiation through the at least one observation window and detect a radiation signal emitted from at least one individual droplet.
Aspect 5. The system of any one of aspects 2-4, further comprising a control module comprising processing circuitry configured to at least: receive an analyte measurement from the analytical instrument; and at least one of: adjust a rate of delivery of the fluid of at least one chemical reservoir; or adjust a volume of delivery of the fluid of at least one chemical reservoir.
Aspect 6. The system of aspect 5, wherein the control module dynamically adjusts operation of the chemical handling module or the reactor module during synthesis of the nanoparticle.
Aspect 7. The system of aspect 6, wherein the dynamic adjustment is in response to machine learning analysis of the real-time characterization data.
Aspect 8. The system of any one of aspects 1-7, wherein each of the plurality of chemical reservoirs comprises a syringe pump.
Aspect 9. The system of any one of aspects 1-8, wherein at least one of the plurality of chemical reservoirs comprises an oil.
Aspect 10. The system of any one of aspects 1-9, wherein the mixer is a static mixer.
Aspect 11. The system of any one of aspects 1-10, wherein the flow reactor is further configured to control environmental conditions of the channel.
Aspect 12. The system of any one of aspects 1-11, wherein the flow reactor is further configured to control the temperature of the channel.
Aspect 13. The system of any one of aspects 1-12, wherein the system further comprises a filtration device configured to prevent the formation of bubbles in the fluids within the system.
Aspect 14. A method comprising: mixing together at least two fluids, thereby forming a segmented flow comprising a plurality of droplets; flowing the segmented flow into a reactor; controlling a temperature of the reactor; measuring at least one target property of nanoparticles in individual droplets in the segmented flow as the plurality of droplets pass through the reactor; and adjusting formation of droplets added to the segmented flow based upon the measured at least one target property of the nanoparticles.
Aspect 15. The method of aspect 14, wherein the at least one target property is selected from nanoparticle radius, nanoparticle size distribution, absorption spectra, photoluminescence characteristics, dispersity status, dynamic viscosity, electrical conductivity, small angle X-ray scattering (SAXS) intensity (Int), crystallinity, and a combination thereof.
Aspect 16. The method of aspect 14 or aspect 15, wherein a rate of delivery of the two fluids, a volume of delivery of the two fluids, or a combination thereof is adjusted based on the measured at least one target property.
Aspect 17. The method of any one of aspects 14-16, wherein the at least two fluids are sequentially mixed via a rotary valve.
Aspect 18. The method of any one of aspects 14-17, wherein the segmented flow comprises the plurality of droplets separated by oil segments.
Aspect 19. The method of any one of aspects 14-18, wherein formation of the droplets is dynamically adjusted to within a defined limit of the at least one target property.
Aspect 20. The method of aspect 19, wherein the dynamic adjustment is in response to machine learning analysis of the measured at least one target property.
From the foregoing, it will be seen that aspects herein are well adapted to attain all the ends and objects hereinabove set forth together with other advantages which are obvious and which are inherent to the structure.
While specific elements and steps are discussed in connection to one another, it is understood that any element and/or steps provided herein is contemplated as being combinable with any other elements and/or steps regardless of explicit provision of the same while still being within the scope provided herein.
It will be understood that certain features and subcombinations are of utility and may be employed without reference to other features and subcombinations. This is contemplated by and is within the scope of the claims.
Since many possible aspects may be made without departing from the scope thereof, it is to be understood that all matter herein set forth or shown in the accompanying drawings and detailed description is to be interpreted as illustrative and not in a limiting sense.
It is also to be understood that the terminology used herein is for the purpose of describing particular aspects only and is not intended to be limiting. The skilled artisan will recognize many variants and adaptations of the aspects described herein. These variants and adaptations are intended to be included in the teachings of this disclosure and to be encompassed by the claims herein.
Now having described the aspects of the present disclosure, in general, the following Examples describe some additional aspects of the present disclosure. While aspects of the present disclosure are described in connection with the following examples and the corresponding text and figures, there is no intent to limit aspects of the present disclosure to this description. On the contrary, the intent is to cover all alternatives, modifications, and equivalents included within the spirit and scope of the present disclosure.
The following examples are put forth so as to provide those of ordinary skill in the art with a complete disclosure and description of how the compounds, compositions, articles, devices and/or methods claimed herein are made and evaluated, and are intended to be purely exemplary of the disclosure and are not intended to limit the scope of what the inventors regard as their disclosure. Efforts have been made to ensure accuracy with respect to numbers (e.g., amounts, temperature, etc.), but some errors and deviations should be accounted for. Unless indicated otherwise, parts are parts by weight, temperature is in ° C. or is at ambient temperature, and pressure is at or near atmospheric.
i. Fluidics Platform with In-Situ X-ray Scattering for Real-Time Autonomous Synthesis and Optimization of Colloidal Nanoparticles
Conventionally, nanoparticle synthesis has relied heavily on empirical knowledge and human intuition, with limited understanding of underlying reaction mechanisms. This approach is inefficient and hinders the targeted discovery of novel nanoparticles, particularly when exploring new systems without prior knowledge. This challenge is compounded by the high sensitivity of nanoparticle formation to synthetic conditions: small changes in reaction environments can yield dramatically different properties. Moreover, the common practice of incrementally modifying existing protocols restricts innovation and comprehensive parameter space exploration, ultimately impeding mechanistic understanding.
Real-time, continuous optimization through the synergistic integration of automation and machine learning (ML) offers an avenue for accelerating the colloidal synthesis of nanoparticles. In recent years, several AI-guided synthesis platforms have emerged, with two main approaches: robot-assisted and fluidics systems. However, these platforms face several fundamental limitations that restrict their effectiveness in materials discovery and optimization. Robotic platforms typically reply on in-situ optical characterization tools such as UV-visible (UV-Vis) or photoluminescence spectroscopy. This dependence on optical techniques not only limits material scope but, due to mobility constraints and space requirements, also makes integration with more sophisticated analytical techniques like synchrotron-based X-ray characterization challenging. Meanwhile, fluidics-based platforms offer greater portability and versatility, as demonstrated in their successful self-driven synthesis of metal nanoparticles and quantum dots using both optical and X-ray techniques. However, current approaches face multiple challenges: they often employ sequential protocols that limit experimental throughput, and they typically separate synthesis from characterization, necessitating sample extraction or transfer for analysis. These limitations not only risk altering products before characterization but also create delayed feedback loops that compromise optimization efficiency and restrict parameter space exploration.
Herein is discussed a self-driven fluidics platform for on-the-fly autonomous synthesis of colloidal nanoparticles that seamlessly integrates automated fluidics, in-line characterization, real-time analysis, and artificial intelligence (AI)-driven active learning. The system incorporates three improvements. First, its modular and mobile design enables direct integration with synchrotron-based SAXS/WAXS, providing comprehensive morphological and structural characterization capabilities beyond traditional optical methods. Second, real-time materials characterization and data analysis are fully integrated into the synthesis process, eliminating the need for sample extraction or post-processing steps. Third, the segmented droplet approach enables parallel reactions, with multiple droplets carrying out different syntheses simultaneously while preventing cross-contamination.
The synthesis of gold nanoparticles was selected to demonstrate the platform's capability to efficiently explore vast synthesis spaces while revealing fundamental insights into nanoparticle formation mechanisms. Additionally, ML is used to investigate the correlations between the synthesis parameters and the resulting nanoparticle properties, providing deeper chemical insights into the mechanisms of nanoparticle formation. For example, past studies have established that the molar ratio of sodium citrate to gold chloride plays a significant role in determining the size of gold nanoparticles, but these studies are primarily limited to a narrow range of ratios, typically between 1:1 and 1:10. The present fluidic platform rapidly and efficiently explores a much broader range of ratios, from approximately 0.27 to 480, while additionally varying other parameters such as pH. After only ˜150 experiments, uniform nanoparticles were synthesized with a target size in a self-driven fashion. This work showcases the successful implementation of an on-the-fly autonomous synthesis of gold nanoparticles, demonstrating a strategy for accelerating nanomaterials discovery and optimization using a self-driven fluidic platform. This approach is general and establishes a general framework applicable to a broad range of colloidal systems.
Workflow of the self-driven fluidics platform for “on-the-fly” autonomous synthesis of colloidal nanoparticles. A workflow diagram of the self-driven fluidic platform is depicted in
To validate this autonomous synthesis approach, the Turkevich method was used for gold nanoparticles synthesis as the model system. As shown in
One feature of the disclosed system is its ability to conduct high-throughput synthesis, using a steady and continuous flow, while maintaining precise control and monitoring of individual reactions. This capability, however, presents a challenge: accurately matching characterization data with specific chemical recipes and reaction conditions for each droplet. After it is created, each droplet must move through the reactor for some amount of time before being characterized. At any point, the reactor can have well over 20 droplets moving through it. An “off-by-one” error in correctly pairing droplets between creation and characterization could misassign data, leading to inaccurate steps in the ML model optimization. Such errors compromise the integrity of the entire dataset, as the ML model relies heavily on accurate input-output relationships to refine synthesis parameters effectively.
This challenge was solved by developing a real-time correlation analysis method that leverages distinctive features in the WAXS data. The oil phase exhibits a characteristic scattering feature within the wave vector transfer (q) range from 1 to 3 Å−1, which is distinct from the aqueous reaction phase. By performing pairwise correlation with a reference oil pattern (see insert 1 (in-situ X-ray measurements demonstrating distinct WAXS features between oil phase (t0) and aqueous droplet (t1) in q-range 1-3 Å−1) and insert 2 (real-time phase recognition via correlation analysis of WAXS profile against reference oil profile, enabling binary identification) of
The real-time analysis pipeline processes experimental SAXS data by assuming a spherical form factor model to extract size (radius), and size distribution. The results of this fitting procedure are fed directly to the ML module (insert 3 (automated droplet data selection and SAXS analysis using spherical form factor models to extract nanoparticle properties (size, r, and distribution, c) during continuous synthesis) of
Experimental Run 1: Autonomous synthesis of monodisperse nanoparticle. The initial experiment (“Run 1”) was designed to explore the synthesis parameter space toward single-objective optimization: minimizing nanoparticle size distribution(s). To balance exploration and exploitation, a GPML model was employed with the Upper Confidence Bound (UCB) acquisition function (B=100, which weights the GP standard deviation 10 times more than the mean), using 1/σ as the optimization target within the accessible synthesis parameter space. The experiments began with 27 droplets, corresponding to the number of droplets spanning the tubing from the point where chemical reagents were mixed at room temperature to the X-ray probing position through the high-temperature reactor. Those droplets were generated using randomly selected parameters from three stock solutions: 10 mM Na Citrate (NCit), a pH controller (either 10 mM HCl or NaOH), and 2 mM HAuCl4(HAu). Synthesis parameters were constrained within practical bounds: NCit and HAu volumes between 1 μL and 30 μL, pH adjustment (HCl or NaOH) from 0 μL to 30 μL, maintaining total volume at 40 μL with buffer solution. At a typical flow rate of 80 L/min, each droplet generated approximately 30 frames of SAXS and WAXS data with 1 frame/see data collections rate.
Except in the case of pure exploration, the primary objective of BO is to find the global maximum of the objective function, not to create an accurate surrogate model over the entire space. Despite this, strong agreement between the experimental data, and the predicted 1/σ values from GPML model was observed. This similarity highlights the effective performance of the GPML model in guiding the optimization process and reflects the balance between exploration and exploitation chosen in the chosen acquisition function, even though perfect accuracy across the entire space is not expected.
To address the questions of how quickly and effectively the GPML model learns, maximum observed 1/σ value was analyzed as a function of training data size. As shown in
The effectiveness of this optimization is directly evidenced by the SAXS profiles at key stages.
Relative opportunity cost (ROC) analysis was used for quantitative assessment of optimization performance and was defined as the relative difference in expectation between true best input and the predicted best input at a given experimental number N, using the following equation:
where x* is the true best input, and ftruth(x*) represents the true maximum value of objective function over the entire feasible space. Since it is infeasible to experimentally evaluate the entire synthetic parameter space (˜19 k possible conditions), x* and ftruth(x*) were derived using the final GPML model trained with all available experimental data. xN,* is the input corresponding to the maximum of the GPML model at a given experiment Nand ftruth(xN,*) is the corresponding objective function value.
To validate the approach against traditional methods, a comparative grid scan experiment (265 points) was conducted within parameters ranges typical of literature studies with narrow pH variations. While grid scan provided exhaustive coverage within its confined space, the resulting GPML struggled to predict beyond these boundaries, with its optimal recipe (NCit: 15 μL, NaOH: 3 μL, HAu: 14 μl) remaining within conventional limits.
Experimental Run 2: Autonomous synthesis of monodispersed nanoparticles with targeted size. Building upon the successful optimization of the size distribution in Run 1, the approach was extended to simultaneously control both particle size and distribution through a single objective function. For Run 2, the following objective function was defined that combines these two properties:
where k is a scale factor (set to 0.5), r is the observed nanoparticle radius from SAXS analysis, r* is the target radius (set to 15 nm), and σ is the observed size distribution of nanoparticles from SAXS analysis. As shown in
Using the objective function to guide the GPML model, an additional 113 autonomous experiments were conducted with active learning, maintaining the same UCB acquisition function with B=100 as Run 1. The new results (shown in
These results demonstrate that the autonomous platform can effectively optimize multiple properties through carefully designed single objective functions, providing a powerful approach for targeted nanoparticle synthesis. The platform successfully navigated the synthesis space to discover conditions yielding larger particles while maintaining relatively narrow size distributions, accessing parameter regions well beyond those explored in conventional protocols.
Understanding the relationship between reaction conditions and nanoparticle properties. The autonomous platform not only enables rapid synthesis of target nanoparticles but also facilitates comprehensive exploration of synthesis-property relationships through in-situ SAXS/WAXS measurements.
To quantitatively assess these relationships, a Pearson correlation coefficients (PCC) analysis was used, as displayed in
Notably, nanoparticle radius and size distributions show no clear correlation with individual reagent volumes, indicating complex, non-linear dependencies on synthesis parameters.
To deconvolute these relationships, the SHAP approach was used to quantify how each reagent volume contributes to the various outcomes (SAXS intensity, size distribution(s), radius, and crystallinity), enabling global understanding of the primary features driving observed results.
The combination of PCC and SHAP analyses reveals the complex interplay between chemical recipes and nanoparticle properties. Although the Turkevich method has been studied for almost half a century, most work has been limited to narrow synthesis spaces (NCit:HAu ratios in 1-10) with minimal pH investigation, reflecting the current synthesis paradigms heavily reliant on existing protocols. In contrast, the disclosed platform effectively mapped a vastly larger space with NCit:HAu from 0.27 to 480 while simultaneously varying pH using very limited experimental iterations.
Conclusions. In this example, an AI-guided fluidics platform equipped with in-situ X-ray scattering was investigated, enabling on-the-fly autonomous synthesis of colloidal nanoparticles. This platform integrates direct in-situ characterization of as-synthesized products via SAXS/WAXS measurements, thereby eliminating the need for sample extraction and mitigating the risks associated with ex-situ processes that can alter nanoproducts before characterization. Using gold nanoparticles as a proof-of-concept, the production of monodisperse nanoparticles was optimized from a set of approximately 19 k possibilities in just 150 experiments over 10 hours, and extended the approach to explore new synthetic regimes, enabling simultaneous control of both size and distribution. This balance of exploration and optimization allows for a systematic investigation of a wide range of parameters, including NCit:HAu ratios from 0.27 to 480 and large pH variations, generating high-quality datasets that enhance understanding of synthesis mechanisms and aid in refining computational and theoretical models. Through comprehensive PCC and SHAP analyses, complex, non-linear relationships between synthesis parameters and nanoparticle properties are revealed. The modular design and adaptability of the disclosed platform suggest its potential for broad applications across various solution-based chemical synthesis systems, potentially accelerating the discovery and optimization of advanced materials.
Materials. Sodium citrate (NCit), hydrogen chloride (HCl), sodium hydroxide (NaOH), tween20, chloroauric acid (HAu), and silicone oil were purchased from Sigma Aldrich. Stock solutions, including 16 mM NCit, 2 mM HAu, 10 mM HCl, 10 mM NaOH, 0.01 wt % tween20 in deionized (DI) water, and silicone oil, were prepared and transferred to 10 mL of gas-tight glass syringes (Hamilton Inc) for automated nanoparticle synthesis.
Fluidic platform. The fluidic platform enables high-throughput nanoparticle synthesis with in-situ SAXS/WAXS characterization. As shown in
Methods-Automated nanoparticle synthesis & characterization. Reagent solutions (NCit, tween in DI water, HCl, NaOH, and HAu) were precisely injected using automated syringes with precise controlled volumes (precision <1 μL) and flow rate at 80 μL/min through selection valves into the main flow loop, then undergoing well-mixing by static mixer. Subsequent oil injections created segmented flow (oil-in-aqueous phases) for high-throughput, parallel synthesis. Synthesis parameters were constrained to minimum 1 mL NCit/HAu, variable HCl/NaOH, 1 μL step size, 40 μL total volume (balanced with 0.01% tween20), and 60 μL oil spacing. Reaction conditions were maintained at 100° C., with as-synthesized products being probed at ˜10 minutes. In-situ SAXS/WAXS (beamline details provided below) provided real-time size/distribution information, feeding ML models that suggested subsequent conditions to enable on-the-fly close-loop process.
For ex-situ analysis, samples from 10 identical droplets were collected. About 300 μL of nanoparticle suspensions were used for UV-Vis (Lambda 35) measurements, and 2-3 μL were deposited and dried on Cu grid for TEM (JEOL 1400).
Machine learning (ML). A Gaussian-process (GP) model was used to map synthesis-property relationships, with Bayesian optimization (BO) guiding subsequent experiments. The Upper Confidence Bound (UCB) acquisition function was used:
where μ(x) and σ(x) represent the GP mean and standard deviation at point x, respectively. β=100 was set in BO to achieve balanced exploration and exploitation, as this provides a naturally conservative strategy that prioritizes finding points with high uncertainty while maintaining exploitation potential. The model implementation used open-source Python libraries GPyTorch and BoTorch, incorporating customized penalty functions and workflow designs specific to the autonomous synthesis. Three reagent volumes of NCit, pH (HCl as negative or NaOH as positive values), and HAu were used as input parameters and the characterization outcome (1/σ) or objective-function value) was used as the output parameters. During autonomous operations, the system optimized the acquisition function at each experimental step using the current GP fit, with the function's maximum determining subsequent experimental conditions.
Synchrotron Beamline Setup for Self-Driven Fluidic Platform. The in-situ SAXS and WAXS were conducted at the SMI (12-ID) and CMS (11-BM) beamlines at the National Synchrotron Light Source II (NSLS II), Brookhaven National Laboratory (BNL). At the SMI beamline, SAXS data were collected using a beam energy of 16.1 keV and beam size of 200×30 um with a Pilatus 1M area detector (Dectris, Switzerland). The detector, consisting of 0.172 mm square pixels in a 981×1043 array, was placed five meters downstream from the sample position. WAXS data were collected with a PILATUS3 900 KW detector (Dectris, Switzerland), featuring 0.172 mm square pixels in a 1475×619 array. To enable simultaneous collection of SAXS and WAXS, the WAXS detector, mounted on an arc, was rotated 15 degrees relative to the incident beam direction with a sample-to-detector distance being 275 mm.
At the CMS beamline, SAXS data were collected using a beam energy of 13.5 keV and beam size of 200×200 μm with a Pilatus 2M area detector (Dectris, Switzerland). The detector, comprising of 0.172 mm square pixels in a 1475×1679 array, was positioned five meters downstream from the sample. WAXS data were collected with a customized L-shaped Pilatus 800K area detector (Dectris, Switzerland), which comprises 0.172 mm square pixels in a 1043×981 array, placed ˜0.260 meters downstream from the sample position. The L-shaped 800K detector allows the beam to pass through, thus enabling the simultaneous collection of SAXS and WAXS without needing to offset the WAXS detector. Scattering patterns from each detector angle were stitched together using custom-developed software. Typical exposure times were 1 second at the SMI beamline and 15 seconds at the CMS beamline. The 2D SAXS and WAXS patterns, collected continuously during synthesis, were reduced to 1D scattering intensity, I(q), through real-time circular averaging. Here, q represents the wave vector transfer, q=(4π/λ) sin(θ), where λ=0.77 Å is the X-ray wavelength and 2θ is the scattering angle. Scattering angles were calibrated using silver behenate as the standard.
Hardware Implementation.
Pairwise Correlation Analysis. A correlation function was developed to analyze time-resolved WAXS data, based on the ‘pandas. DataFrame.corrwith’ method. This function calculates the Pearson correlation coefficient between two one-dimensional arrays. The correlation was computed using the formula:
where μa and μb are the means, and σa and σb are the standard deviations of the respective arrays. This method enables reliable differentiation between oil and aqueous phases in time-resolved WAXS data based on their distinct WAXS features, thereby providing robust phase identification.
Representative SAXS Data from Run1 and Their Ex-Situ Characterization Results.
Reliability of Real-time SAXS Data Analysis. Out of a total of 249 experimental datasets (Run1), 19 datasets were identified as inaccurately analyzed and subsequently re-analyzed.
Grid Scan Experiment. To further evaluate the efficiency and effectiveness of the AI-driven autonomous synthesis approach, the approach was compared against a traditional grid scan synthetic experiment conducted within a confined synthetic space, which has narrow pH variations similar to those reported in past studies. For the limited parameter ranges, the boundary conditions for pH variation of HCl or NaOH were set between 0 μL to 4 μL. As shown in
3D Mesh Construction of Cost-Fun Values. In
Evaluation of Radius and Cost-Func During Both Run1 and Run2.
In-situ WAXS Characterization. During the autonomous synthesis experiments, in-situ SAXS and WAXS measurements were performed in real-time, simultaneously. In real-time, the WAXS data were mostly used to identify oil- and aqueous-phase by a correlation analysis method. The WAXS data was also used to investigate the crystalline structure of the synthesized gold nanoparticles based on the absence or presence of primary peak in q˜2.68 A−1, which indicates the (111) diffraction peak of face-centered cubic (fcc) structure, with a gold lattice parameter of ˜4.08 A.
2D Map of Nanoparticle Properties Against Reagent Volumes.
Relationship Between Nanoparticle Properties and Molar Ratios of NCit to Hau.
ii. Fluidics Platform for Sequential Growth and Mixing
Enabling Multiple-Step Synthesis by Automated Sequential Injection. A system and method have been developed comprising an optical-sensor triggered injection apparatus that automatically introduces sequential chemical reagents to pre-formed droplets on-demand. The working principle and corresponding optical image of said system are illustrated by the sequential injection of red dye to pre-formed blue dye droplets in oil, as shown in
Enabling Solution Well-Mixing in Droplets. While the design shown in
This mixing capability enables room-temperature synthesis of shape-controlled CuO nanoparticles with highly uniform size and shape characteristics using CuCl2, NaOH, and ascorbic acid. Example products shown in
It should be emphasized that the above-described embodiments of the present disclosure are merely possible examples of implementations set forth for a clear understanding of the principles of the disclosure. Many variations and modifications may be made to the above-described embodiment(s) without departing substantially from the spirit and principles of the disclosure. All such modifications and variations are intended to be included herein within the scope of this disclosure and protected by the following claims.
This application claims priority to and the benefit of U.S. Provisional Application No. 63/620,132, filed on Jan. 11, 2024, which is hereby incorporated herein by reference in its entirety.
The present invention was made with government support under contract number DE-SC0012704 awarded by the U.S. Department of Energy. The United States government may have certain rights in this invention.
| Number | Date | Country | |
|---|---|---|---|
| 63620132 | Jan 2024 | US |
