A novel purification strategy based on functionalized melamine sponge for determining multiple pesticides in complex-matrix American Ginseng.

Remediating acidic mine drainage is essential to mitigate environmental concerns due to its acidity and elevated heavy metal concentration. This study investigates the performance of capacitive deionization (CDI) and membrane CDI (MCDI) for remediating acidic zinc mine tailing leachate from Australian mining sites, specifically focusing on the roles of fabricated carbon-based electrodes and carbon nanotube (CNT) membrane. Among the four fabricated electrodes, activated carbon + carbon nanotubes (AC + CNT) electrode achieved the highest metal removal at a rapid rate (30–65% Al 3+ , Fe 3+ , Zn 2+ , salt adsorption rate 0.78 mg·g −1 ·min −1 ). This was attributed to AC + CNT electrode's superior capacitance (9.0 F·g −1 ), and markedly reduced internal resistances (148.7 Ω, vs AC's 208.2 Ω,), contributing to enhanced ion transport pathways compared to AC and carbon black electrodes. While polymer-coated membrane electrodes initially hindered ion transport, embedding CNTs within the membrane restored charge-transfer pathways, enabling 67–88% removal of major metals (Al 3+ , Fe 3+ , Zn 2+ ) from zinc tailing. Membrane reuse evaluation indicated inevitable metal oxidation, leading to precipitation on membrane. To address this, a granular activated carbon (GAC) pretreatment step was implemented upstream, reducing bulk metal loading by ~62% The integrated configuration of MCDI with CNT membrane upon GAC pretreatment maintained high metal removal efficiencies (97–98%) across multiple cycles of adsorption and chemical-free desorption, while producing an acidic stream (pH 1.1 ± 0.2) suitable for mining reuse. Overall, these findings demonstrate the potential of electrochemical processes for rapid acid mine drainage (AMD) remediation, while concurrently supporting acid recovery. • Combined AC + CNT carbon electrode exhibited superior metal removal in CDI. • CNT membrane MCDI achieved high metal removal from acidic zinc mine tailing. • GAC pretreatment prevented CNT membrane fouling by lowering feed ion concentrations. • GAC–MCDI process achieved 98% metal removal, yielding an acidic effluent. • The electrochemical approach enabled rapid and low-chemical AMD remediation.

In the 5G era, electromagnetic pollution poses an invisible threat to human safety. Given that electromagnetic wave (EMW) absorption invariably generates heat, this phenomenon may induce structural degradation of the material and pose safety risks. It is crucial to develop high-strength, flame-retardant electromagnetic interference (EMI) shielding materials. Herein, we propose a green and sustainable approach to induce stable dispersion of carbon nanotubes (CNTs) with in situ lignin and non-covalent bonding in waste bamboo materials. The in situ lignin released during bamboo fiber pretreatment was utilized as a "bridge" to enhance the interfacial bonding between CNTs and the fibers, and co-assembles with the CNTs through inter-boundary hydrogen bonding, electrostatic stabilization, and π-π stacking. Then, biocomposites were synthesized in one step by hot pressing, using molten in situ lignin as a green binder for self-bonding. The biocomposite exhibits excellent EMI shielding performance (72.5dB), superior mechanical properties, flame retardancy (total heat release reduced by 35.22%, Total smoke production reduced by 81.58%). It offers a sustainable approach to developing multifunctional structural materials for the construction, aerospace, and military sectors.

Films with large anisotropy ratios (r) between the in-plane and cross-plane thermal conductivity (κ) can be used for directional heat spreading in electronics thermal management. Here, we show that commercially available solution-spun carbon nanotube (CNT) films with 20 μm thickness and centimeter-scale lateral dimensions exhibit orthotropic thermal conductivity with the highest reported r to date, reaching r = 1400 ± 160 at room temperature (T). We find r using laser flash thermal diffusivity (α) measurements over a T range from 198 to 573 K. Dedoping of acid residuals via annealing increases the in-plane-aligned αx of dedoped samples by a factor of 2 compared to the doped samples. These dedoped CNT films also display a strong αx ∝ T-1.1 scaling, indicating that phonon-phonon scattering impacts heat transport along the direction of alignment. Our work motivates further exploration of ultrahigh r in macroscopic CNT materials and applications of CNT films for directional heat spreading.

Large-scale carbon nanotube (CNT) synthesis based on floating catalyst chemical vapor deposition (FC-CVD), unlike conventional CVD, utilizes a growth promoter, commonly a sulfur-containing species, whose role in the overall growth process is still poorly understood, hindering more efficient reactor design and a better quality CNT product. By developing a machine-learning interatomic potential here, we conduct atomistic molecular dynamics collision simulations for pure Fe and Fe-S clusters that allow us to directly quantify their sticking probability. Sulfur is found to reduce the intrinsic sticking for small clusters, instead strongly enhancing it for larger sizes. We demonstrate that this crossover is driven by S-induced shape compliance, a mechanism where surface passivation leads to large shape fluctuations that efficiently absorb collision energy. These insights may help rationalize the diverse and sometimes conflicting experimental outcomes reported for S-assisted FC-CVD synthesis of CNTs.

Embedding ultrasmall Ru nanoparticle catalytic sites on Ni3Fe encapsulated carbon nanotubes for efficient and durable water-splitting.

Understanding structure-property relationships in complex materials requires integrating complementary measurements across multiple length scales. Here we propose an interpretable "multimodal" machine learning framework that unifies heterogeneous analytical systems for end-to-end characterization, demonstrated on carbon nanotube (CNT) films whose properties are highly sensitive to microstructural variations. Quantitative morphology descriptors are extracted from SEM images via binarization, skeletonization, and network analysis, capturing curvature, orientation, intersection density, and void geometry. These SEM-derived features are fused with Raman indicators of crystallinity/defect states, specific surface area from gas adsorption, and electrical surface resistivity. Multi-dimensional visualization using radar plots and UMAP reveals clear clustering of CNT films according to crystallinity and entanglements. Regression models trained on the multimodal feature set show that nonlinear approaches, particularly XGBoost, achieve the best predictive accuracy under leave-one-out cross-validation. Feature-importance analysis further provides physically meaningful interpretations: surface resistivity is primarily governed by junction-to-junction transport length scales, crystallinity/defect-related metrics, and network connectivity, whereas specific surface area is dominated by intersection density and void size. The proposed multimodal machine learning framework offers a proof-of-concept demonstration for data-driven and explainable characterization of complex materials using multimodal machine learning.

Advances in understanding natural articular cartilage have led to the development of bionic repair materials, with hydrogel composites emerging as a promising option due to their low friction, high water content, and customizable mechanical properties. This study investigates PVA/PAMPS/FMWCNT hydrogels, focusing on the role of negatively charged groups in enhancing performance. FTIR analysis confirmed the integration of PVA, PAMPS. SEM revealed a porous structure resembling cartilage, with carboxyl-functionalized samples showing the largest pores and achieved a 1853% swelling ratio, while hydroxyl- and amine-functionalized samples had smaller pores and greater crosslink density. Mechanical tests showed hydroxyl-functionalized samples achieved 1.01MPa tensile stress and 237% elongation, whereas carboxyl-functionalized samples, despite strong hydrogen bonding, had inferior mechanical properties due to high porosity. Tribological tests demonstrated carboxyl-functionalized samples had the 0.0346 coefficient of friction (COF), attributed to their high negative charge density and hydration lubrication. Long-term friction tests revealed a stable coefficient (0.07), demonstrating sustained frictional stability under extended sliding conditions. These findings highlight the importance of functionalized multiwall carbon nanotubes (FMWCNT) and negatively charged groups in optimizing hydrogels for cartilage repair, offering insights for developing bionic materials.

Chitosan-based carbonaceous adsorbent for wastewater treatment applications.

One-pot/solvent-free aerobic oxidation of KA oil to ε-caprolactone catalyzed by carbon nanotubes: Integrated alcohol-to-ester pathway, kinetic modeling and economic evaluation

Robust PAM/HACC dual-network hydrogel with hydroxylated carbon nanotubes for strain sensing and electromagnetic interference shielding

Synergy at the nanoscale: enhanced Cr(VI) remediation through the rational integration of ilmenite and carbon nanotubes

The impact of multi walled carbon nanotubes on the mechanical properties of polypropylene fibre reinforced concrete

Adsorption of perfluorocarboxylic acids by carbon nanotube and its derivative: Insight into mechanisms based on experimental study and modeling analysis.

Recent advances in carbon based anti-icing materials: new insights from Hansen Solubility Parameter analysis.

The evolvement of smart cementitious materials with self-monitoring features, evaluating and early warning the structural integrity of infrastructures at an early stage is of great importance both for the service and sustainability aspects. This feasibility study investigates two grades of commercially available continuous single carbon nanotube fiber (CNTF) filaments embedded in a sustainable geopolymer (GP) matrix to assess their potential as piezoresistive elements for mechanical deformation sensing via straightforward two-probe DC electrical resistance measurements. Prior their incorporation, the CNTF filaments were systematically characterized. Afterwards, smart composites were prepared by a simple casting method around the CNTF and the GP reaction was monitored. Thereby, a prominent change of the pure electronic electrical properties was identified upon ageing, exhibiting similar electrical resistance evolution profiles, capturing the transition from fresh mixture to a rigid matrix within 24 h. Subsequently, the integrated CNTFs were utilized as mechanical stress sensors during static and cyclic three-point-bending, as well as compression loading conditions, respectively. The obtained results demonstrate the ability to reliably detect applied mechanical loads through the electrical signal, expressed as the fractional change in electrical resistance (FCR) combined with efficient electrical current transmission independently of environmental influencing factors. Importantly, cyclic compression tests confirmed highly reversible and repeatable electrical responses achieving circa 10% FCR. The outcome indicates that such CNTF/GP multifunctional building materials are capable to be used as durable, built-in and real-time structural health monitoring (SHM) indicators to enable an autonomous maintenance of civil infrastructures. • Multifunctional building structures for sustainable construction • Self-sensing cementitious composites without dispersion issues by single CNTF filaments • Geopolymer-based piezoresistive sensors with pure DC electronic properties • Structural integrity monitoring upon bending and compression modes • Stable static and cyclic piezoresistive response

Plasma-sputtered Pt@CoFe dual single-atom sites on 3D nitrogen-doped carbon nanotube networks enhance the bifunctional electrocatalytic performance of direct methanol fuel cells

Tribological and thermal stability performance of palm oil added with 0.001 wt% ultra-minimal multi-walled carbon nanotubes under sliding operation

Synergistic interfacial hydrogen-bond and electronic structure modulation in quaternized chitosan modified carbon nanotubes enabling efficient H2O2 electrosynthesis

The structure and evolution of electric double layers (EDLs) under nanoscale confinement critically govern interfacial electrochemical processes, particularly in CO2-related electrochemical systems. While extensive studies have explored EDLs formed by ionic liquids (ILs) on planar electrodes, the coupled effects of geometric curvature, slit confinement, and electric potential on EDL formation remain insufficiently understood. Herein, molecular dynamics simulations are employed to investigate the adsorption behavior and EDL characteristics of CO2/IL mixtures confined within curved slit pores formed by concentric carbon nanotubes (CNTs). The slit width and electrode potential are systematically varied to elucidate their roles in regulating molecular arrangement, charge distribution, and interfacial thermodynamics. The results reveal a critical slit width governing EDL formation: under severe confinement (width of silt equals 0.8 nm), spatial restriction suppresses ionic layering, preventing the establishment of a stable EDL. When the slit width increases to 1.2 nm or above, alternating ionic layers emerge, indicating EDL formation accompanied by pronounced CO2 enrichment. Curvature-induced asymmetry leads to stronger adsorption on the outer CNT surface, while electric potential polarity results in distinct screening mechanisms at the cathode and the anode. Notably, CO2 participates directly in anode potential shielding and adopts a preferentially parallel orientation to the electrode surface. Free energy and interfacial entropy analyses further demonstrate that EDL regions coincide with high free energy barriers and reduced molecular freedom, whereas wider slits enable effective potential screening and facilitate ion transport. These findings provide molecular-level insight into the interplay between confinement, curvature, and electrostatics, offering guidance for the rational design of electrochemical interfaces for CO2 capture and conversion.