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Additive manufacturing (AM) is rapidly revolutionizing modern manufacturing with recent progress in advanced printing methods and improved properties of printed materials. However, traditional AM methods are limited by their input‐oriented nature, which demands tedious trial‐and‐error tuning of printing parameters to achieve desired output properties. Here, an output‐oriented artificial intelligence‐integrated AM (AIAM) method is reported that enables an user to specify desired output properties while the printer autonomously discovers the optimal input printing parameters by integrating hybrid machine learning models and in situ measurements. Based on a predictive mapping between the input printing parameters and the output properties of interests established with <20 experiments designed by active learning, inverse design tasks are performed to intelligently generate the printing parameter settings that lead to desired outcomes using reinforcement learning. This method is demonstrated by autonomous aerosol jet printing (AJP) of conductive polymer films and achieving user‐defined electrical resistances with an ultralow error of 3.7%. The AIAM method, with its output‐oriented nature, holds the potential to significantly improve the autonomy, predictability, efficiency, and accessibility of the AM processes, which will unlock new possibilities in the autonomous and intelligent printing of a broad range of functional materials and devices.

 

The advent of 3D printing has facilitated the rapid fabrication of microfluidic devices that are accessible and cost‐effective. However, it remains a challenge to fabricate sophisticated microfluidic devices with integrated structural and functional components due to limited material options of existing printing methods and their stringent requirement on feedstock material properties. Here, a multi‐materials multi‐scale hybrid printing method that enables seamless integration of a broad range of structural and functional materials into complex devices is reported. A fully printed and assembly‐free microfluidic biosensor with embedded fluidic channels and functionalized electrodes at sub‐100 µm spatial resolution for the amperometric sensing of lactate in sweat is demonstrated. The sensors present a sensitive response with a limit of detection of 442 nmand a linear dynamic range of 1–10 mm, which are performance characteristics relevant to physiological levels of lactate in sweat. The versatile hybrid printing method offers a new pathway toward facile fabrication of next‐generation integrated devices for broad applications in point‐of‐care health monitoring and sensing.

 

Optimizing material compositions often enhances thermoelectric performances. However, the large selection of possible base elements and dopants results in a vast composition design space that is too large to systematically search using solely domain knowledge. To address this challenge, a hybrid data‐driven strategy that integrates Bayesian optimization (BO) and Gaussian process regression (GPR) is proposed to optimize the composition of five elements (Ag, Se, S, Cu, and Te) in AgSe‐based thermoelectric materials. Data is collected from the literature to provide prior knowledge for the initial GPR model, which is updated by actively collected experimental data during the iteration between BO and experiments. Within seven iterations, the optimized AgSe‐based materials prepared using a simple high‐throughput ink mixing and blade coating method deliver a high power factor of 2100 µW m⁻¹K⁻², which is a 75% improvement from the baseline composite (nominal composition of Ag₂Se₁). The success of this study provides opportunities to generalize the demonstrated active machine learning technique to accelerate the development and optimization of a wide range of material systems with reduced experimental trials.

 

The development of new materials and their compositional and microstructural optimization are essential in regard to next-generation technologies such as clean energy and environmental sustainability. However, materials discovery and optimization have been a frustratingly slow process. The Edisonian trial-and-error process is time consuming and resource inefficient, particularly when contrasted with vast materials design spaces¹. Whereas traditional combinatorial deposition methods can generate material libraries^2,3, these suffer from limited material options and inability to leverage major breakthroughs in nanomaterial synthesis. Here we report a high-throughput combinatorial printing method capable of fabricating materials with compositional gradients at microscale spatial resolution. In situ mixing and printing in the aerosol phase allows instantaneous tuning of the mixing ratio of a broad range of materials on the fly, which is an important feature unobtainable in conventional multimaterials printing using feedstocks in liquid–liquid or solid–solid phases^4–6. We demonstrate a variety of high-throughput printing strategies and applications in combinatorial doping, functional grading and chemical reaction, enabling materials exploration of doped chalcogenides and compositionally graded materials with gradient properties. The ability to combine the top-down design freedom of additive manufacturing with bottom-up control over local material compositions promises the development of compositionally complex materials inaccessible via conventional manufacturing approaches.

 

Printing techniques using nanomaterials have emerged as a versatile tool for fast prototyping and potentially large‐scale manufacturing of functional devices. Surfactants play a significant role in many printing processes due to their ability to reduce interfacial tension between ink solvents and nanoparticles and thus improve ink colloidal stability. Here, a colloidal graphene quantum dot (GQD)‐based nanosurfactant is reported to stabilize various types of 2D materials in aqueous inks. In particular, a graphene ink with superior colloidal stability is demonstrated by GQD nanosurfactants via the π–π stacking interaction, leading to the printing of multiple high‐resolution patterns on various substrates using a single printing pass. It is found that nanosurfactants can significantly improve the mechanical stability of the printed graphene films compared with those of conventional molecular surfactant, as evidenced by 100 taping, 100 scratching, and 1000 bending cycles. Additionally, the printed composite film exhibits improved photoconductance using UV light with 400 nm wavelength, arising from excitation across the nanosurfactant bandgap. Taking advantage of the 3D conformal aerosol jet printing technique, a series of UV sensors of heterogeneous structures are directly printed on 2D flat and 3D spherical substrates, demonstrating the potential of manufacturing geometrically versatile devices based on nanosurfactant inks.

 

Flexible thermoelectric (TE) devices hold great promise for energy harvesting and cooling applications, with increasing significance to serve as perpetual power sources for flexible electronics and wearable devices. Despite unique and superior TE properties widely reported in nanocrystals, transforming these nanocrystals into flexible and functional forms remains a major challenge. Herein, demonstrated is a transformative 3D conformal aerosol jet printing and rapid photonic sintering process to print and sinter solution‐processed Bi₂Te_2.7Se_0.3nanoplate inks onto virtually any flexible substrates. Within seconds of photonic sintering, the electrical conductivity of the printed film is dramatically improved from nonconductive to 2.7 × 10⁴S m⁻¹. The films demonstrate a room temperature power factor of 730 µW m⁻¹K⁻², which is among the highest values reported in flexible TE films. Additionally, the film shows negligible performance changes after 500 bending cycles. The highly scalable and low‐cost fabrication process paves the way for large‐scale manufacturing of flexible devices using a variety of high‐performing nanoparticle inks.

 

Printing is a versatile method to transform semiconducting nanoparticle inks into functional and flexible devices. In particular, thermoelectric nanoparticles are attractive building blocks to fabricate flexible devices for energy harvesting and cooling applications. However, the performance of printed devices are plagued by poor interfacial connections between nanoparticles and resulting low carrier mobility. While many rigid bulk materials have shown a thermoelectric figure of meritZTgreater than unity, it is an exacting challenge to develop flexible materials withZTnear unity. Here, a scalable screen‐printing method to fabricate high‐performance and flexible thermoelectric devices is reported. A tellurium‐based nanosolder approach is employed to bridge the interfaces between the BiSbTe particles during the postprinting sintering process. The printed BiSbTe flexible films demonstrate an ultrahigh room‐temperature power factor of 3 mW m⁻¹K⁻²andZTabout 1, significantly higher than the best reported values for flexible films. A fully printed thermoelectric generator produces a high power density of 18.8 mW cm⁻²achievable with a small temperature gradient of 80 °C. This screen‐printing method, which directly transforms thermoelectric nanoparticles into high‐performance and flexible devices, presents a significant leap to make thermoelectrics a commercially viable technology for a broad range of energy harvesting and cooling applications.

 

Solution‐processable semiconducting 2D nanoplates and 1D nanorods are attractive building blocks for diverse technologies, including thermoelectrics, optoelectronics, and electronics. However, transforming colloidal nanoparticles into high‐performance and flexible devices remains a challenge. For example, flexible films prepared by solution‐processed semiconducting nanocrystals are typically plagued by poor thermoelectric and electrical transport properties. Here, a highly scalable 3D conformal additive printing approach to directly convert solution‐processed 2D nanoplates and 1D nanorods into high‐performing flexible devices is reported. The flexible films printed using Sb₂Te₃nanoplates and subsequently sintered at 400 °C demonstrate exceptional thermoelectric power factor of 1.5 mW m⁻¹K⁻²over a wide temperature range (350–550 K). By synergistically combining Sb₂Te₃2D nanoplates with Te 1D nanorods, the power factor of the flexible film reaches an unprecedented maximum value of 2.2 mW m⁻¹K⁻²at 500 K, which is significantly higher than the best reported values for p‐type flexible thermoelectric films. A fully printed flexible generator device exhibits a competitive electrical power density of 7.65 mW cm⁻²with a reasonably small temperature difference of 60 K. The versatile printing method for directly transforming nanoscale building blocks into functional devices paves the way for developing not only flexible energy harvesters but also a broad range of flexible/wearable electronics and sensors.

 

Thermoelectric materials, which can convert waste heat into electricity or act as solid‐state Peltier coolers, are emerging as key technologies to address global energy shortages and environmental sustainability. However, discovering materials with high thermoelectric conversion efficiency is a complex and slow process. The emerging field of high‐throughput material discovery demonstrates its potential to accelerate the development of new thermoelectric materials combining high efficiency and low cost. The synergistic integration of high‐throughput material processing and characterization techniques with machine learning algorithms can form an efficient closed‐loop process to generate and analyze broad datasets to discover new thermoelectric materials with unprecedented performances. Meanwhile, the recent development of advanced manufacturing methods provides exciting opportunities to realize scalable, low‐cost, and energy‐efficient fabrication of thermoelectric devices. This review provides an overview of recent advances in discovering thermoelectric materials using high‐throughput methods, including processing, characterization, and screening. Advanced manufacturing methods of thermoelectric devices are also introduced to realize the broad impacts of thermoelectric materials in power generation and solid‐state cooling. In the end, this article also discusses the future research prospects and directions.

 

Flexible thermoelectric generators (TEGs) have shown immense potential for serving as a power source for wearable electronics and the Internet of Things. A key challenge preventing large-scale application of TEGs lies in the lack of a high-throughput processing method, which can sinter thermoelectric (TE) materials rapidly while maintaining their high thermoelectric properties. Herein, we integrate high-throughput experimentation and Bayesian optimization (BO) to accelerate the discovery of the optimum sintering conditions of silver–selenide TE films using an ultrafast intense pulsed light (flash) sintering technique. Due to the nature of the high-dimensional optimization problem of flash sintering processes, a Gaussian process regression (GPR) machine learning model is established to rapidly recommend the optimum flash sintering variables based on Bayesian expected improvement. For the first time, an ultrahigh-power factor flexible TE film (a power factor of 2205 μW m −1 K −2 with a zT of 1.1 at 300 K) is demonstrated with a sintering time less than 1.0 second, which is several orders of magnitude shorter than that of conventional thermal sintering techniques. The films also show excellent flexibility with 92% retention of the power factor (PF) after 10 3 bending cycles with a 5 mm bending radius. In addition, a wearable thermoelectric generator based on the flash-sintered films generates a very competitive power density of 0.5 mW cm −2 at a temperature difference of 10 K. This work not only shows the tremendous potential of high-performance and flexible silver–selenide TEGs but also demonstrates a machine learning-assisted flash sintering strategy that could be used for ultrafast, high-throughput and scalable processing of functional materials for a broad range of energy and electronic applications.