Carbon Letters最新文献

The convergence of artificial intelligence (AI) and carbon nanotube (CNT) chemistry is accelerating innovations in the synthesis, functionalization, and advanced applications of carbon-based nanomaterials. This review highlights recent AI-driven methodologies, including neural networks, ensemble learning, metaheuristics, and hybrid frameworks that are redefining the design, surface engineering, and structure–property relationships of CNTs. Special attention is given to their roles in clean energy technologies, polymer nanocomposites, environmental systems, and nanoelectronics. Advances such as autonomous synthesis guided by deep learning, high-throughput experimentation, and AI-enabled property prediction are critically reviewed. Challenges including data fragmentation, class imbalance, and lack of benchmarking are addressed, alongside future directions such as physics-informed machine learning, robotics integration, and multi-objective optimization. This review positions AI as a disruptive catalyst in advanced CNT research, offering intelligent automation and predictive insights across diverse carbon-material applications.

Multi-walled carbon nanotubes (MWCNTs), widely recognized for their excellent electrical, thermal, and mechanical properties, have gained significant attention across various fields. However, aggregation due to their high Van der Waals force limits their processability and applications. Surface functionalization can offer a solution to these limitations and broadens their potential uses. Nevertheless, the low reaction rates of covalent functionalization in conventional batch reactors, caused by MWCNT aggregation, remain a significant challenge to overcome. Moreover, controlling the degree of functionalization in batch reactors is difficult. In this work, we demonstrated covalent functionalization of MWCNT with 4-bromoaniline to produce MWCNT-PhBr with high, controllable degrees of functionalization, using a customized packed-bed flow reactor. The reaction was performed under various conditions, including the reactant injection sequence, reaction time (1–3 h), temperature (60–80 ℃), and concentration of reactant (0.42–1.72 M). The resulting degree of functionalization was controlled within the range of 1.81–5.70 wt%. Notably, the highest degree of functionalization obtained under the optimized flow conditions (5.70 wt% at 70 °C and 1.72 M) represents a 2.11-fold increase compared to that achieved under the optimized batch conditions (2.70 wt% at 60 °C and 0.86 M). These results suggest that the flow system is an efficient and reliable method for synthesizing functionalized MWCNTs with the desired functional group content.

Pharmaceutical waste directly contributes to environmental pollution. Ibuprofen is one of the pharmaceutical wastes that often ends up in the environment without proper treatment, causing various harmful effects. Here, carbon quantum dots/titanium dioxide (CQDs/TiO₂) nanocomposites synthesis as a photocatalyst for ibuprofen photodegradation has been investigated. This research consists of 3 steps: the first was initiated with CQD synthesis using the hydrothermal method derived from banana peel extract, followed by the synthesis of TiO₂ rutile and anatase with banana peel extract as the reducing agent. The last was CQDs/TiO₂ nanocomposites synthesis using a hydrothermal method. The CQDs/TiO₂ nanocomposites obtained were characterized by TEM, FTIR, XRD, SEM-EDX, and UV-Visible spectroscopy. Bright green fluorescence of CQDs was observed under UV irradiation with a average size of 9.5 nm. The nanocomposite of CQD/TiO₂ rutile and CQD/TiO₂ anatase exhibited crystalline structures, each displaying a diffraction pattern of rutile and anatase, indicating high purity. However, CQDs/TiO₂ anatase has a smaller size of 8 nm than CQD/TiO₂ rutile of 132 nm. Therefore, the CQDs/TiO₂ anatase nanocomposite showed the most effectiveness as a photocatalyst in degrading ibuprofen, with a photodegradation percentage of up to 53.656% at pH 3, a slight photocatalyst mass of 0.1 g, and a short photodegradation time of 30 min.

Lithium-ion batteries (LIBs) are widely used as key components in electric vehicles (EVs) and energy storage systems (ESS) owing to their high energy density, long cycle life, and stable operation. The rapid expansion of the EV market has intensified the demand for advanced graphite anode materials that combine cost competitiveness with superior electrochemical performance, including fast-charging capability and structural stability. This study presents an integrated approach for optimizing the physical spheronization of natural graphite and synthesizing a high-performance coating pitch (CP) for chemical spheronization. The correlation between mechanical stress and morphological evolution during the process was quantitatively analyzed using an Air Classifier Mill (ACM). Optimal spheronization was achieved by aligning theoretically calculated stress levels (≈ 3.72 MPa for particle rounding and > 14.86 MPa for fracture) with experimental results. High-performance coating pitches were prepared via stepwise polymerization of pyrolysis fuel oil (PFO), followed by Thin Layer Evaporation (TLE)-based molecular weight distribution tailoring. The resulting pitch exhibited a softening point of 279.4 °C, coking value of 70.7%, and zero quinoline insoluble (QI) content, and was applied as a coating precursor. The optimized spheronized graphite anode showed excellent electrochemical properties, including an initial Coulombic efficiency of 92.6% and 97.4% capacity retention after 50 cycles. Electrochemical impedance spectroscopy (EIS) and galvanostatic intermittent titration technique (GITT) analyses further confirmed efficient lithium-ion diffusion at both the surface and core, demonstrating suitability for fast-charging LIB applications.

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The rapid development of electronic devices towards higher power density and miniaturization, has made heat dissipation a critical challenge that limits their reliability and lifespan. This study presents the preparation of natural flake graphite (NFG)-based composites, synergistically modulated by intermediate-phase carbon microspheres (MCMB) and spherical graphite (SG), via micro-hot pressing 3D additive molding technology. This innovative approach addresses the limitations of traditional metal-based heat dissipation materials. The pore structure and properties of the composites were optimized by varying the contents of MCMB and spherical graphite. The results demonstrated that the composites exhibited superior overall performance when 40 wt% MCMB and 20 wt% SG were incorporated. The compressive strength significantly increased to 32.71 MPa, while the thermal conductivity in the parallel and perpendicular directions reached 351.08 W/(m·K) and 296.16 W/(m·K), respectively, and the anisotropy ratio was reduced to 1.18. Mechanistic analysis revealed that MCMB reduced anisotropy by disrupting the orientation of the NFG lamellae, while SG optimized the pore structure and established a multistage thermal conductivity pathway. The synergistic effects of both components resulted in a unified material with high thermal conductivity, low thermal expansion, and strong mechanical properties, offering a novel solution for the thermal management of electronic devices.

This work evaluated the microstructural, electrochemical and anti-corrosion properties of Cu-doped diamond-like carbon (DLC) coatings deposited using the plasma-enhanced chemical vapor deposition (PECVD) technique. The coatings were synthesized with varying Cu concentrations (0.68, 2.25,10.28, 22.32, and 40.1 at.%) to investigate their effect on film characteristics. The structural and chemical composition of the coatings was analysed using X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, field emission scanning electron microscopy (FESEM), atomic force microscopy (AFM), and X-ray diffraction (XRD). The electrochemical performance was evaluated using open circuit potential (OCP), electrochemical impedance spectroscopy (EIS), and potentio dynamic polarization (PP) tests in a 3.5 wt% NaCl solution. The XPS and Raman analysis revealed that Cu incorporation altered the sp²/sp³ hybridization ratio, increasing graphitization at higher concentrations. The results demonstrated that DLC-Cu₂.₂₅ exhibited the lowest surface roughness (Ra = 7 nm), enhancing its corrosion resistance due to its dense, uniform structure and passivating CuO layer. The electrochemical results showed that the corrosion potential shifted towards a more noble direction (-640 mV), and the corrosion rate decreased to 0.003 mm/year, confirming improved stability. However, at higher Cu concentrations, Cu segregation led to increased roughness (Ra = 30 nm) and localized corrosion due to the formation of Cu-rich clusters. Incorporation of Cu improves the corrosion resistance of DLC coatings, although at higher Cu levels some corrosion still occurs. These findings highlight that moderate Cu incorporation optimally enhances protective performance, whereas excessive Cu content compromises corrosion resistance. A mechanistic description of the corrosion-resistance behaviour of Cu-DLC coatings is proposed.

Graphene-reinforced aluminum (Gr/Al) nanocomposites offer exceptional mechanical properties for aerospace, automotive, and electronics applications. Precise estimation of their characteristics, including ultimate tensile strength (UTS) and Young’s modulus (YM), remains challenging due to complex atomic interactions and computational limitations of traditional methods. This study proposes a novel machine learning framework combining Molecular Dynamics (MD) simulations, Adaptive Fast Desensitized Kalman Filter (AFDKF), Diffusion Variational Graph Neural Network (DV-GNN), and Arctic Tern Optimizer (ATO) for efficient and accurate mechanical property prediction. Important variables such as graphene alignment, volume fraction, chirality, and ambient temperature are captured by the method. DV-GNN achieves a prediction accuracy of 99.9%, significantly outperforming existing ML models. The framework also demonstrates low error rates, fast computation, and scalability, providing a robust computational tool for intelligent design of high-strength, lightweight Gr/Al nanocomposites.

To develop high-performance lubricants, this study investigates polyamide 6 (PA6) as a solid additive in urea-based greases (UBGs). The tribological performance of UBGs containing varying concentrations of PA6 was evaluated at different temperatures using an HSR-2 M high-speed reciprocating friction and wear tester. The structure and composition of PA6 and the worn surfaces were characterized using scanning electron microscopy, thermo-gravimetric analysis, Fourier transform infrared spectroscopy, 3D laser scanning microscopy, X-ray photoelectron spectroscopy, and focused ion beam techniques to elucidate the underlying friction and wear mechanisms. The results demonstrate that PA6 significantly enhances the lubricating performance of UBGs. At an optimal concentration of 0.2 wt% PA6 and 40 °C, the friction coefficient was reduced by 7.6%, and the wear volume decreased by 50.9% compared to base UBG. This improvement is attributed to the formation of a tribofilm on the steel surface, driven by interactions between PA6 microspheres and the grease matrix. Upon contact, PA6 adsorbs and deposits on the steel surface, promoting the formation of a continuous lubricating film. This film enhances the film-forming capacity of the base grease, minimizes direct ball–disk contact, and effectively reduces friction and wear.

The growing demand for high-capacity lithium-ion batteries, driven by the development of compact portable devices and long-range electric vehicles, necessitates significant advancements in electrode design. Increasing electrode thickness is among the most effective strategies for enhancing performance and reducing manufacturing costs in lithium-ion batteries. However, thick electrodes are often prone to delamination, crack formation, and elevated internal resistance resulting from extended lithium-ion diffusion pathways. In this work, a simple and efficient method for fabricating thick electrodes is demonstrated by using lithium iron phosphate slurry–coated carbon nanotube (CNT) films, yielding bifunctional CNT-based thick electrodes (BCTE) that enable simultaneous electron transport through the interconnected CNT network and Li⁺ percolation through electrolyte-filled porous pathways formed by the CNT-film architecture. Electrochemical measurements reveal that the CNT film–based electrodes exhibit a lithium-ion diffusion coefficient nearly twice that of conventional thick electrodes even when normalized to total carbon content, confirming that the performance enhancement originates from the CNT architecture rather than from carbon loading differences. The specific capacity of 148.3 mAh g⁻¹ is maintained under a substantial active material mass loading (135 mg cm⁻²), with superior rate capability and stable Coulombic efficiency at a current density of 1 mA cm⁻². These findings underscore the potential of this facile fabrication approach for enabling advanced thick electrode architectures, supporting the broader adoption of high performance lithium-ion batteries.

The global decline in access to safe and potable water is a growing social concern with increasing relevance projected to escalate in the coming decades. Different techniques for purifying water have been developed and tested to address this issue. More recently, there has been a technological trend in adopting carbon nanotube (CNT) technology for wastewater treatment. However, due to the immature state of research in the area, several aspects remain. In response to these challenges, this study conducts a bibliometric analysis to assess the current knowledge regarding the utilization and advancement of CNTs in wastewater treatment. The study utilised various software applications to map the relevant literature published between 2013 and 2023, encompassing 699 scholarly articles that underwent content analysis. The findings revealed how the knowledge on the topic is organized, highlighting key collaboration patterns among authors, institutions, and journals. They also identified the most influential authors, providing valuable insights into the networks and dynamics that shape research progress in this area. The research findings also reveal the primary technological trends in utilizing CNTs for water treatment and the challenges that hinder their practical application. To guide advancements in this field, the article contributes to indicating future research avenues systematically organized into three critical categories: (i) investigations targeting the most frequently identified literature gaps, (ii) studies focusing on process optimization and operational efficiencies, and (iii) exploration of emerging technologies and evolving trends. This structured approach provides a clear roadmap for overcoming existing barriers and unlocking the full potential of CNTs in wastewater treatment applications. This article can serve as a reference for subsequent technological investigations.