Machine Learning Guided Synthesis of Flash Graphene

Electric Field Effects in Flash Joule Heating Synthesis

Flash Joule heating has emerged as an ultrafast, scalable, and versatile synthesis method for nanomaterials, such as graphene. Here, we experimentally and theoretically deconvolute the contributions of thermal and electrical processes to the synthesis of graphene by flash Joule heating. While traditional methods of graphene synthesis involve purely chemical or thermal driving forces, our results show that the presence of charge and the resulting electric field in a graphene precursor catalyze the formation of graphene. Furthermore, modulation of the current or the pulse width affords the ability to control the three-step phase transition of the material from amorphous carbon to turbostratic graphene and finally to ordered (AB and ABC-stacked) graphene and graphite. Finally, density functional theory simulations reveal that the presence of a charge- and current-induced electric field inside the graphene precursor facilitates phase transition by lowering the activation energy of the reaction. These results demonstrate that the passage of electrical current through a solid sample can directly drive nanocrystal nucleation in flash Joule heating, an insight that may inform future Joule heating or other electrical synthesis strategies.

Designing semiconductor materials and devices in the post-Moore era by tackling computational challenges with data-driven strategies

In the post-Moore's law era, the progress of electronics relies on discovering superior semiconductor materials and optimizing device fabrication. Computational methods, augmented by emerging data-driven strategies, offer a promising alternative to the traditional trial-and-error approach. In this Perspective, we highlight data-driven computational frameworks for enhancing semiconductor discovery and device development by elaborating on their advances in exploring the materials design space, predicting semiconductor properties and optimizing device fabrication, with a concluding discussion on the challenges and opportunities in these areas.

Machine learning guided tuning charge distribution by composition in MOFs for oxygen evolution reaction

Traditional design/optimization of metal-organic frameworks (MOFs) is time-consuming and labor-intensive. In this study, we utilize machine learning (ML) to accelerate the synthesis of MOFs. We have built a library of over 900 MOFs with different metal salts, solvent ratios, reaction durations and temperatures, and utilize zeta potentials as target variables for ML training. A total of four ML models have been used to train the collected dataset and assess their convergence performances, where Random Forest Regression (RFR) and Gradient Boosting Regression (GBR) models show strong correlation and accurate predictions. We then predicted two kinds of MOFs from RFR and GBR models. Remarkably, the experimentally data of the synthesized MOFs closely matched the predicted results, and these MOFs exhibited excellent electrocatalytic performances for oxygen evolution. This study would have general implications in the utilization of machine learning for accelerating the synthesis of MOFs for diverse applications.

Text Mining the Literature to Inform Experiments and Rationalize Impurity Phase Formation for BiFeO3

We used data-driven methods to understand the formation of impurity phases in BiFeO3 thin-film synthesis through the sol-gel technique. Using a high-quality dataset of 331 synthesis procedures and outcomes extracted manually from 177 scientific articles, we trained decision tree models that reinforce important experimental heuristics for the avoidance of phase impurities but ultimately show limited predictive capability. We find that several important synthesis features, identified by our model, are often not reported in the literature. To test our ability to correctly impute missing synthesis parameters, we attempted to reproduce nine syntheses from the literature with varying degrees of "missingness". We demonstrate how a text-mined dataset can be made useful by informing new controlled experiments and forming a better understanding for impurity phase formation in this complex oxide system.

Preparation of Coal-Based Graphene by Flash Joule Heating

This study explores the production of flash graphene (AC-FG) from anthracite coal by using the flash Joule heating (FJH) method. This study demonstrates that AC-FG can be derived from anthracite coal by precisely controlling the system parameters, specifically the pulse voltage. The FJH process requires no catalyst. The produced material was characterized by using Raman, XRD, XPS, TG, SEM, TEM, and XPS techniques. The results reveal that the degree of graphitization of coal reaches its peak at 190 V. From an energy perspective, FJH provides a straightforward and cost-effective method for graphene preparation, offering a substantial avenue for the efficient utilization of coal resources and the cost-effective application of graphene.

Replacement of Concrete Aggregates with Coal-Derived Flash Graphene

Each year, the growth of cities across developing economies in Asia, Africa, and Latin America drives demand for concrete to house and serve their burgeoning populations. Since 1950, the number of people living in urban areas has quadrupled to 4.2 billion, with another predicted 2.5 billion expected to join them in the next three decades. The largest component of concrete by volume is aggregates, such as sand and rocks, with sand as the most mined material in the world. However, the extraction rate of sand currently exceeds its natural replenishment rate, meaning that a global concrete-suitable sand shortage is extremely likely. As such, replacements for fine aggregates, such as sand, are in demand. Here, flash Joule heating (FJH) is used to convert coal-derived metallurgical coke (MC) into flash graphene aggregate (FGA), a blend of MC-derived flash graphene (MCFG), which mimics a natural aggregate (NA) in size. While graphene and graphene oxide have previously been used as reinforcing additives to concrete, in this contribution, FGA is used as a total aggregate replacement for NA, resulting in 25% lighter concrete with increases in toughness, peak strain, and specific compressive strength of 32, 33, and 21%, respectively, with a small reduction in specific Young's modulus of 11%. FJH can potentially enable the replacement of fine NA with FGA, resulting in lighter, stronger concrete.

Biomass-Derived Carbonaceous Materials with Graphene/Graphene-Like Structures: Definition, Classification, and Environmental Applications

Biomass-derived carbonaceous materials with graphene/graphene-like structures (BGS) have attracted tremendous attention in the field of environmental remediation. The introduction of graphene/graphene-like structures into raw biochars can effectively improve their properties, such as electrical conductivity, surface functional groups, and catalytic activity. In 2021, the International Organization for Standardization defined graphene as a "single layer of carbon atoms with each atom bound to three neighbours in a honeycomb structure". Considering this definition, several studies have incorrectly referred to BGS (e.g., biomass-derived few-layer graphene or porous graphene-like nanosheets) as "graphene". The definitions and classifications of BGS and their applications in environmental remediation have not been assessed critically thus far. Comprehensive analysis and sufficient and robust evidence are highly desired to accurately determine the specific structures of BGS. In this perspective, we provide a systematic framework to define and classify the BGS. The state-of-the-art methods currently used to determine the structural properties of BGS are scrutinized. We then discuss the design and fabrication of BGS and how their distinctive features could improve the applicability of biomass-derived carbonaceous materials, particularly in environmental remediation. The environmental applications of these BGS are highlighted, and future research opportunities and needs are identified. The fundamental insights in this perspective provide critical guidance for the further development of BGS for a wide range of environmental applications.

Synthesis of Clean Hydrogen Gas from Waste Plastic at Zero Net Cost

Hydrogen gas (H2 ) is the primary storable fuel for pollution-free energy production, with over 90 million tonnes used globally per year. More than 95% of H2 is synthesized through metal-catalyzed steam methane reforming that produces 11 tonnes of carbon dioxide (CO2 ) per tonne H2 . "Green H2 " from water electrolysis using renewable energy evolves no CO2 , but costs 2-3× more, making it presently economically unviable. Here catalyst-free conversion of waste plastic into clean H2 along with high purity graphene is reported. The scalable procedure evolves no CO2 when deconstructing polyolefins and produces H2 in purities up to 94% at high mass yields. The sale of graphene byproduct at just 5% of its current value yields H2 production at a negative cost. Life-cycle assessment demonstrates a 39-84% reduction in emissions compared to other H2 production methods, suggesting the flash H2 process to be an economically viable, clean H2 production route.

An explainable machine-learning approach for revealing the complex synthesis path-property relationships of nanomaterials

Machine learning (ML) models have recently shown important advantages in predicting nanomaterial properties, which avoids many trial-and-error explorations. However, complex variables that control the formation of nanomaterials exhibiting the desired properties still need to be better understood owing to the low interpretability of ML models and the lack of detailed mechanism information on nanomaterial properties. In this study, we developed a methodology for accurately predicting multiple synthesis parameter-property relationships of nanomaterials to improve the interpretability of the nanomaterial property mechanism. As a proof-of-concept, we designed glutathione-gold nanoclusters (GSH-AuNCs) exhibiting an appropriate fluorescence quantum yield (QY). First, we conducted 189 experiments and synthesized different GSH-AuNCs by varying the thiol-to-metal molar ratio and reaction temperature and time in reasonable ranges. The fluorescence QY of GSH-AuNCs could be systematically and independently programmed using different experimental parameters. We used limited GSH-AuNC synthesis parameter data to train an extreme gradient boosting regressor model. Moreover, we improved the interpretability of the ML model by combining individual conditional expectation, double-variable partial dependence, and feature interaction network analyses. The interpretability analyses established the relationship between multiple synthesis parameters and fluorescence QYs of GSH-AuNCs. The results represent an essential step towards revealing the complex fluorescence mechanism of thiolated AuNCs. Finally, we constructed a synthesis phase diagram exceeding 6.0 × 104 prediction variables for accurately predicting the fluorescence QY of GSH-AuNCs. A multidimensional synthesis phase diagram was obtained for the fluorescence QY of GSH-AuNCs by searching the synthesis parameter space in the trained ML model. Our methodology is a general and powerful complementary strategy for application in material informatics.

Machine Learning-Assisted Large-Area Preparation of MoS2 Materials

Molybdenum disulfide (MoS2) is a layered transition metal-sulfur compound semiconductor that shows promising prospects for applications in optoelectronics and integrated circuits because of its low preparation cost, good stability and excellent physicochemical, biological and mechanical properties. MoS2 with high quality, large size and outstanding performance can be prepared via chemical vapor deposition (CVD). However, its preparation process is complex, and the area of MoS2 obtained is difficult to control. Machine learning (ML), as a powerful tool, has been widely applied in materials science. Based on this, in this paper, a ML Gaussian regression model was constructed to explore the growth mechanism of MoS2 material prepared with the CVD method. The parameters of the regression model were evaluated by combining the four indicators of goodness of fit (r2), mean squared error (MSE), Pearson correlation coefficient (p) and p-value (p_val) of Pearson's correlation coefficient. After comprehensive comparison, it was found that the performance of the model was optimal when the number of iterations was 15. Additionally, feature importance analysis was conducted on the growth parameters using the established model. The results showed that the carrier gas flow rate (Fr), molybdenum sulfur ratio (R) and reaction temperature (T) had a crucial impact on the CVD growth of MoS2 materials. The optimal model was used to predict the size of molybdenum disulfide synthesis under 185,900 experimental conditions in the simulation dataset so as to select the optimal range for the synthesis of large-size molybdenum disulfide. Furthermore, the model prediction results were verified through literature and experimental results. It was found that the relative error between the prediction results and the literature and experimental results was small. These findings provide an effective solution to the preparation of MoS2 materials with a reduction in the time and cost of trial and error.

Upcycling of Waste Plastic into Hybrid Carbon Nanomaterials

Graphitic 1D and hybrid nanomaterials represent a powerful solution in composite and electronic applications due to exceptional properties, but large-scale synthesis of hybrid materials has yet to be realized. Here, a rapid, scalable method to produce graphitic 1D materials from polymers using flash Joule heating (FJH) is reported. This avoids lengthy chemical vapor deposition and uses no solvent or water. The flash 1D materials (F1DM), synthesized using a variety of earth-abundant catalysts, have controllable diameters and morphologies by parameter tuning. Furthermore, the process can be modified to form hybrid materials, with F1DM bonded to turbostratic graphene. In nanocomposites, F1DM outperform commercially available carbon nanotubes. Compared to current 1D material synthetic strategies using life cycle assessment, FJH synthesis represents an 86-92% decrease in cumulative energy demand and 92-94% decrease in global-warming potential. This work suggests that FJH affords a cost-effective and sustainable route to upcycle waste plastic into valuable 1D and hybrid nanomaterials.

Synthesis and Functionalization of Graphene Materials for Biomedical Applications: Recent Advances, Challenges, and Perspectives

Since its discovery in 2004, graphene is increasingly applied in various fields owing to its unique properties. Graphene application in the biomedical domain is promising and intriguing as an emerging 2D material with a high surface area, good mechanical properties, and unrivalled electronic and physical properties. This review summarizes six typical synthesis methods to fabricate pristine graphene (p-G), graphene oxide (GO), and reduced graphene oxide (rGO), followed by characterization techniques to examine the obtained graphene materials. As bare graphene is generally undesirable in vivo and in vitro, functionalization methods to reduce toxicity, increase biocompatibility, and provide more functionalities are demonstrated. Subsequently, in vivo and in vitro behaviors of various bare and functionalized graphene materials are discussed to evaluate the functionalization effects. Reasonable control of dose (<20 mg kg^-1 ), sizes (50-1000 nm), and functionalization methods for in vivo application are advantageous. Then, the key biomedical applications based on graphene materials are discussed, coupled with the current challenges and outlooks of this growing field. In a broader sense, this review provides a comprehensive discussion on the synthesis, characterization, functionalization, evaluation, and application of p-G, GO, and rGO in the biomedical field, highlighting their recent advances and potential.

Tunable Hybridized Morphologies Obtained through Flash Joule Heating of Carbon Nanotubes

Hybrid carbon nanomaterials, such as those that incorporate carbon nanotubes into graphene sheets, have been found to display interesting mechanical and electrical properties because of their covalent bonding and π-π stacking domains. However, synthesis of these hybrid materials is limited by the high energetic cost of techniques like chemical vapor deposition. Here, we demonstrate the solvent- and gas-free synthesis of a 2D carbon nanotube/graphene network through flash Joule heating of pristine carbon nanotubes. The relative proportion of each morphology in the hybrid material can be tuned by varying the pulse time, as confirmed by Raman spectroscopy and microscopy. Triboindentation of epoxy composites made with the hybrid material shows increases of 162% and 64% to the hardness and Young's modulus, respectively, compared with the neat epoxy. These results demonstrate that flash Joule heating can be used to inexpensively convert carbon nanotubes into a hybrid network of nanotubes and graphene for use as an effective reinforcing additive in epoxy composites.

The Application of Machine Learning: Controlling the Preparation of Environmental Materials and Carbon Neutrality

The greenhouse effect is a severe global problem [...].

Machine Learning-Assisted Synthesis of Two-Dimensional Materials

Two-dimensional (2D) materials have intriguing physical and chemical properties, which exhibit promising applications in the fields of electronics, optoelectronics, as well as energy storage. However, the controllable synthesis of 2D materials is highly desirable but remains challenging. Machine learning (ML) facilitates the development of insights and discoveries from a large amount of data in a short time for the materials synthesis, which can significantly reduce the computational costs and shorten the development cycles. Based on this, taking the 2D material MoS₂ as an example, the parameters of successfully synthesized materials by chemical vapor deposition (CVD) were explored through four ML algorithms: XGBoost, Support Vector Machine (SVM), Naïve Bayes (NB), and Multilayer Perceptron (MLP). Recall, specificity, accuracy, and other metrics were used to assess the performance of these four models. By comparison, XGBoost was the best performing model among all the models, with an average prediction accuracy of over 88% and a high area under the receiver operating characteristic (AUROC) reaching 0.91. And these findings showed that the reaction temperature (T) had a crucial influence on the growth of MoS₂. Furthermore, the importance of the features in the growth mechanism of MoS₂ was optimized, such as the reaction temperature (T), Ar gas flow rate (R_f), reaction time (t), and so on. The results demonstrated that ML assisted materials preparation can significantly minimize the time spent on exploration and trial-and-error, which provided perspectives in the preparation of 2D materials.

Brushed Metals for Rechargeable Metal Batteries

Battery designs are swiftly changing from metal-ion to rechargeable metal batteries. Theoretically, metals can deliver maximum anode capacity and enable cells with improved energy density. In practice, these advantages are only possible if the parasitic surface reactions associated with metal anodes are controlled. These undesirable surface reactions are responsible for many troublesome issues, like dendrite formation and accelerated consumption of active materials, which leads to anodes with low cycle life or even battery runaway. Here, a facile and solvent-free brushing method is reported to convert powders into films atop Li and Na metal foils. Benefiting from the reactivity of Li metal with these powder films, surface energy can be effectively tuned, thereby preventing parasitic reaction. In-operando study of P₂ S₅ -modified Li anodes in liquid electrolyte cells reveals a smoother electrode contour and more uniform metal electrodeposition and dissolution behavior. The P₂ S₅ -modified Li anodes sustain ultralow polarization in symmetric cell for >4000 h, ≈8× longer than bare Li anodes. The capacity retention is ≈70% higher when P₂ S₅ -modified Li anodes are paired with a practical LiFePO₄ cathode (≈3.2 mAh cm^-2 ) after 340 cycles. Brush coating opens a promising avenue to fabricate large-scale artificial solid-electrolyte-interphase directly on metals without the need for organic solvent.

Turbostratic Boron-Carbon-Nitrogen and Boron Nitride by Flash Joule Heating

Turbostratic layers in 2D materials have an interlayer misalignment. The lack of alignment expands the intrinsic interlayer distances and weakens the optical and electronic interactions between adjacent layers. This introduces properties distinct from those structures with well-aligned lattices and strong coupling interactions. However, direct and rapid synthesis of turbostratic materials remains a challenge owing to their thermodynamically metastable properties. Here, a flash Joule heating (FJH) method to achieve bulk synthesis of boron-carbon-nitrogen ternary compounds with turbostratic structures by a kinetically controlled ultrafast cooling process that takes place within milliseconds (10³  to 10⁴ K s^-1 ) is reported. Theoretical calculations support the existence of turbostratic structures and provide estimates of the energy barriers with respect to conversion into the corresponding well-aligned counterparts. When using non-carbon conductive additives, a direct synthesis of boron nitride is possible. The turbostratic nature facilitates mechanical exfoliation and more stable dispersions. Accordingly, the addition of flash products to a poly(vinyl alcohol) nanocomposite film coating a copper surface greatly improves the copper's resistance to corrosion in 0.5 m sulfuric acid or 3.5 wt% saline solution. FJH allows the use of bulk materials as reactants and provides a rapid approach to large quantities of the hitherto hard-to-access turbostratic materials.