Size Of Nanoparticles Research Articles

Neuro Fuzzy in Predicting the Characteristics of Some Nanomaterials

Unveiling the impressive capabilities of the Adaptive Neuro-Fuzzy Inference System (ANFIS), this study effectively predicts key properties of engineered nanomaterials, opening doors to innovative applications across various industries. We initially investigate the cytotoxic effects of TiO2 and ZnO nanoparticles on immortalized human lung epithelial cells, employing ANFIS to establish correlations between nanoparticle size and behaviour in different media and the resulting cellular membrane damage, quantified by lactate dehydrogenase release. Next, to predict the compressive strength of geopolymers, analysing over previous experimental datasets focused on critical chemical ratios. This model demonstrates its capability to optimize formulations for enhanced mechanical performance in sustainable construction materials. Additionally, we apply ANFIS to evaluate the size of silver nanoparticles in montmorillonite/starch bio nanocomposites, identifying significant factors such as AgNO3 concentration. The ANFIS models achieved high accuracy across all applications, underscoring their utility in predicting material behaviour and optimizing formulations for improved performance and safety. Collectively, these findings illustrate the potential of ANFIS as a robust tool in nanomaterial research and development.

A General Approach for Metal Nanoparticle Encapsulation Within Porous Oxides

AbstractEncapsulation of metal nanoparticles within oxide materials has been shown as an effective strategy to improve activity, selectivity, and stability in several catalytic applications. Several approaches have been proposed to encapsulate nanoparticles, such as forming core‐shell structures, growing ordered structures (zeolites or metal‐organic frameworks) on nanoparticles, or directly depositing support materials on nanoparticles. Here, a general nanocasting method is demonstrated that can produce diverse encapsulated metal@oxide structures with different compositions (Pt, Pd, Rh) and multiple types of oxides (Al2O3, Al2O3‐CeO2, ZrO2, ZnZrOx, In2O3, Mn2O3, TiO2) while controlling the size and dispersion of nanoparticles and the porous structure of the oxide. Metal@polymer structures are first prepared, and then the oxide precursor is infiltrated into such structures and the resulting material is calcined to form the metal@oxide structures. Most Pt@oxides catalysts show similar catalytic activity, demonstrating the availability of surface Pt sites in the encapsulated structures. However, the Pt@Mn2O3 sample showed much higher CO oxidation activity, while also being stable under aging conditions. This work demonstrated a robust nanocasting method to synthesize metal@oxide structures, which can be utilized in catalysis to finely tune metal‐oxide interfaces.

Fabrication of Multifunctional Green-Synthesized Copper Oxide Nanoparticles Using Rumex vesicarius L. Leaves for Enhanced Photocatalytic and Biomedical Applications

Recently, the use of plant extracts has emerged as an innovative approach for the production of various nanoparticles. Enhancing green methods for synthesizing copper oxide (CuO) nanoparticles (NPs) is a key focus in the field of nanotechnology. This study presents a novel and eco-friendly synthesis of CuO NPs using Rumex vesicarius L. leaf extracts, offering a cost-effective and efficient method. The synthesized CuO NPs were evaluated for their cytotoxic effects against human cervical carcinoma (HeLa) cells, as well as their photocatalytic and antimicrobial activities. The morphology, size, and structural properties of the CuO NPs were characterized using various analytical techniques. X-ray diffraction (XRD) analysis confirmed the pure crystalline structure of the CuO NPs with a size of 19 nm, while transmission electron microscopy (TEM) showed particle sizes ranging from 5 to 200 nm. The photocatalytic performance of the CuO NPs was assessed through the photodegradation of crystal violet (CV) and methylene blue (MB) dyes under UV light. The NPs exhibited excellent decolorization efficiency, effectively degrading dyes in aqueous solutions under irradiation. Furthermore, the green-synthesized CuO NPs displayed strong antibacterial and antifungal activities against a variety of human pathogens. They also demonstrated significant dose-dependent cytotoxicity against the HeLa cancer cell line, with an IC50 value of 8 ± 0.54 μg/mL.

Evaluation of Antibacterial Activity of Bismuth Ferrites Nanoparticles in the Inhibition of E. Coli and S. Aureus Bacteria.

In this work, bismuth ferrites (BFO) nanoparticles were produced in the form of using sol-gel technique, followed by annealing in a tube furnace in temperatures from 400 °C to 650 °C. X-ray diffraction (XRD) results showed the formation of small sizes nanoparticles (NPs) with high purity. Structural analysis displayed that annealing at 600 °C could make BFO NPs be fitted to rhombohedral space group (R3c), with small quantity of spurious phases. The sizes of the BFO nanoparticles determined by transmission electron microscopy (HRTEM) are between 50 to 100 nm. To evaluate the efficiency of BFO in antimicrobial susceptibility tests, the nanoparticles were dispersed through cand tested agar diffusion method and dilution in a 96 well plate using a Gram-positive strains (Staphylococcus aureus) and Gram negative strain (Escherichia coli). The antibacterial activity of the BFO NPs was tested at concentrations of 2 mg/mL with MIC greater than 60 μg/mL for both bacteria.

Enhanced surface emission of bismuth-ion doped glass by adding metal nanoparticles

Enhancing the luminescent properties of doped silica glass has garnered significant interest due to its potential applications in photonics and optoelectronics. In this study, we enhance the surface emission of bismuth(Bi)-doped silica glass by incorporating metal nanoparticles. Utilizing finite-difference time-domain (FDTD) simulations, we observed a significant increase in surface emission following population inversion. We developed a novel algorithm to achieve a uniform distribution of nanoparticles in a two-dimensional computational model, ensuring that the distribution is physically accurate. Through systematic investigation, we explored the effects of the number, distribution, and size of silver nanoparticles on the surface emission enhancement of Bi-doped glass. Our results demonstrate that under optimal conditions, the surface emission enhancement can reach nearly 72%. Additionally, we evaluated the impact of various metal nanoparticles, finding that gold, silver, copper, and platinum positively influence surface emission enhancement, while titanium has an inhibitory effect. This study underscores the potential of metal nanoparticles to significantly improve the luminescent properties of doped glass, paving the way for advanced applications in photonic devices.

Recent Advances in Simulation Studies on the Protein Corona

When flowing through the blood stream, drug carriers such as nanoparticles encounter hundreds of plasma proteins, forming a protein layer on the nanoparticle surface, known as the “protein corona”. Since the protein corona influences the size, shape, and surface properties of nanoparticles, it can modulate their circulating lifetime, cytotoxicity, and targeting efficiency. Therefore, understanding the mechanism of protein corona formation at the atomic scale is crucial, which has become possible due to advances in computer power and simulation methodologies. This review covers the following topics: (1) the structure, dynamics, and composition of protein corona on nanoparticles; (2) the effects of protein concentration and ionic strength on protein corona formation; (3) the effects of particle size, morphology, and surface properties on corona formation; (4) the interactions among lipids, membranes, and nanoparticles with the protein corona. For each topic, mesoscale, coarse-grained, and all-atom molecular dynamics simulations since 2020 are discussed. These simulations not only successfully reproduce experimental observations but also provide physical insights into the protein corona formation. In particular, these simulation findings can be applied to manipulate the formation of a protein corona that can target specific cells, aiding in the rational design of nanomedicines for drug delivery applications.

Photocatalytic Degradation of Four Organic Dyes Present in Water Using ZnO Nanoparticles Synthesized with Green Synthesis Using Ambrosia ambrosioides Leaf and Root Extract

Currently, several organic dyes found in wastewater cause severe contamination problems for flora, fauna, and people in direct contact with them. This research proposes an alternative for the degradation of polluting dyes using ZnO nanoparticles (NPs) synthesized by an ecological route using leaf and root extracts of Ambrosia ambrosioides as a reducing agent (with a weight/volume ratio = 4%). Scanning Electron Microscopy (SEM) was used to determine the morphology, showing an agglomeration of cluster-shaped NPs. Using Transmission Electron Microscopy (TEM), different sizes of NPs ranging from 5 to 56 nm were observed for both synthesized NPs. The composition and structure of the nanomaterial were analyzed by infrared spectroscopy (FT-IR) and X-ray diffraction (XRD), showing as a result that the NPs have a wurtzite-like crystalline structure with crystallite sizes around 32–37 nm for both samples. Additionally, the bandgap of the NPs was calculated using Ultraviolet Visible Spectroscopy (UV–Vis), determining values of 2.82 and 2.70 eV for the NPs synthesized with leaf and root, respectively. Finally, thermogravimetric analysis demonstrated that the nanoparticles contained an organic part after the green synthesis process, with high thermal stability for both samples. Photocatalytic analysis showed that these nanomaterials can degrade four dyes under UV irradiation, reaching 90% degradation for methylene blue (MB), methyl orange (MO) and Congo red (CR) at 60, 100 and 60 min, respectively, while for methyl red (MR) almost 90% degradation was achieved at 140 min of UV irradiation. These results demonstrate that it is effective to use Ambrosia ambrosioides root and leaf extracts as a reducing agent for the formation of ZnO NPs, also evidencing their favorable application in the photocatalytic degradation of these four organic dyes.

Effect of N-Doped Carbon on the Morphology and Oxygen Reduction Reaction (ORR) Activity of a Xerogel-Derived Mn(II)O Electrocatalyst

This work synthesizes a xerogel from a sol–gel synthesis strategy and supports it on N-doped carbon support from spent coffee biomass (Mn(II)O/N-CC, hereafter MnO) as an efficient oxygen reduction reaction (ORR) catalyst in alkaline electrolytes. The effects of N-CC carbon content on MnO nanoparticle size, dispersion, distribution, morphology, and electrochemistry on ORR are discussed. The SEM and TEM measurements show that increasing the N-CC content during the MnO gelation reaction improved MnO dispersion and particle size during thermal treatment, increasing the ORR’s electrochemical active surface area. Several physiochemical and electrochemical characterizations show a clear relationship between N-CC catalysts and ORR activities. The best catalyst, MnO/N-CC-5, had an even distribution of 27 nm MnO nanoparticles on the N-CC support. The MnO/N-CC-5 catalyst had almost identical ORR kinetics and stability to those of the state-of-the-art Pt/C catalyst in 0.1 M KOH electrolytes, losing only 10 mV in half-wave potential after 5000 potential cycles and retaining 96% of current for over 10 h of continuous chronoamperometric stability. By measuring the electrochemical active surface areas of various catalysts by cyclic voltammetry at different scan rates and measuring the double layer capacitance (Cdl) and ECSA, MnO/N-CC-5 catalysts were shown to have enhanced ORR activity. The XPS analysis explains the ORR activity in terms of the Mn3+/Mn4+ ratio, and a mechanism was proposed. These findings suggest that the MnO/N-CC-5 catalyst could be a cathode catalyst in fuel cells, biofuel cells, metal–air batteries, and other energy conversion devices.

Manufacturing Aluminum-Based Nanocomposites via Stir-Squeeze Casting Combined with Ultrasonication

This study successfully produced aluminum nanocomposites using a stir-squeeze casting process, both with and without ultrasonication (US) assistance. The matrix material utilized was scrap automobile wheel aluminum alloy (A356), with 1% SiC nano particles, averaging a size of 40 nm, serving as the reinforcement material. A comparison was made by also producing A356 aluminum casts with and without the use of US. The produced casts underwent thorough chemical and mechanical characterization, including optical and scanning microscopy, porosity measurement, hardness measurement, compression and tensile testing, as well as wear testing. Additionally, energy-dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD) analyses were conducted to assess compositions and confirm the presence of SiC nano particles in the aluminum matrix. Porosity levels were slightly higher in the nanocomposite samples compared to pure matrix samples, attributed to the tendency of pore formation due to improper distribution of ceramic particles, resulting in clustering and agglomeration. However, significant reduction in porosity was observed with the application of ultrasonication, effectively breaking up clusters and agglomerations of reinforcement particles. Regarding mechanical properties, the A356+SiC sample with US exhibited the highest hardness (70.8 HRB), tensile strength (163.25 MPa), and compressive strength (387.2 MPa), along with the lowest abrasive wear loss (0.0017 g) among all types of casts produced in this study.

Magnetic Effects in Textile Fibers Filled with Nanoconstituents Based on Iron Oxides

ABSTRACT The paper proves the possibility of providing magnetic properties in the process of saturation of textile fibers with a nanomixture of divalent and trivalent iron oxides. The mechanisms of interaction of nanostructures with natural and synthetic fibers are analyzed. The peculiarities of the introduction of magnetic components into flax, cotton, polyamide, and polyester fibers are determined. The relative number of particles with nanoscale in the total was statistically proved. To evaluate the macro effects of saturation, the relative amount of the accumulated mixture of divalent and trivalent iron oxides was determined, taking into account the previous fiber weight. Two basic constants characterizing the properties of textile fibers to magnetic interaction are proposed. It is proved that flax fibers exhibit the highest magnetic efficiency. The average size of iron oxide nanoparticles is 80–90 nanometers, which is confirmed by studies using electron microscopy. The final magnetization of the obtained textile materials provides an attractive magnetization of 20–30 mPascal. The magnetic field of magnetic textile fibers acts at a height of up to 6–7 mm. The directions of the use of fibers with magnetic properties are shown as a prospect for the development of textile smart products.

Solid-state solvatochromism of oxazolidine in multi-functionalized copolymer nanoparticles: Development of advanced materials with multi-color fluorescence

Investigating the solvatochromism of stimuli-chromic compounds such as oxazolidine in polymer matrices with various polarities or functionalities is a unique approach to developing advanced materials for anticounterfeiting, sensor, optoelectronic, and displayers. In a novel strategy, multi-functionalized copolymer nanoparticles were synthesized by copolymerization of methyl methacrylate (MMA) with various functional comonomers in emulsion media for post-polymerization modification with oxazolidine and investigation of its solvatochromism in both colloidal solution and solid phase. The particle size and morphology of nanoparticles were influenced significantly by the polarity of functional groups, and the morphology evolution from sphere to anisotropic shapes was observed by increasing the polarity of functional groups. Investigation of oxazolidine solvatochromism in colloidal solution and solid polymer powders indicated different mechanisms for solvatochromism, in which the polarity of media is the main effective parameter in colloidal solution and the polarity of functional groups in the polymer structure is the main effective parameter in solid polymer phase. The solvatochromism of oxazolidine was confirmed by observed red shift and blue shift in UV–Vis and fluorescence spectra, in which the intensity of absorbance and emission peaks was changed as a function of the polarity of functional groups. Solvatochromic photoluminescent polymer nanoparticles were used for several advanced applications such as solid anticounterfeiting inks to information encryption, dual-mode visualization of latent fingerprints, and also development of organic light-emitting diodes (OLEDs). For the first time, the results indicate a significant effect of polymer chain local polarity induced by functional groups on the tuning of optical properties of the oxazolidine by the solid-state solvatochromic phenomenon. Solvatochromism of oxazolidine in functionalized polymer nanoparticles is an interesting approach to developing novel intelligent materials that have advanced applications in different fields such as anticounterfeiting and information encryption, optoelectronic, polarity sensor, and visualization of latent fingerprints.

Extraction of methamphetamine and pseudoephedrine by modified graphene oxide solid phase extraction method coupled to HPLC-UV in urine sample.

Methamphetamine, pseudoephedrine, and related drugs are among the first drugs used for the stimulation of the central nervous system. In this study, two adsorbents based on graphene oxide (GO) were synthesized and used for the analysis of methamphetamine and pseudoephedrine. The prepared nano-adsorbents based on GO in this study were coated by polyaniline (PANI) and Fe3O4/C-nanodot/GO (magnetic adsorbent). The average size of nanoparticles (GO/PANI) was 18.43nm. The specific surface area and pore size diameter of Fe3O4/C-nanodot/GO were 22.71 m2 g- 1 and 15.23nm, respectively. Experimental variables affecting the extraction efficiency of the analytes such as pH of the sample solution, amount of adsorbent, extraction time, and type of eluents were investigated and optimized by response surface methodology. Under optimum conditions, GO/PANI and Fe3O4/C-nanodot/GO were considered appropriate solid phase extraction adsorbents for HPLC-based analyses of the studied drugs in human urine samples. However, GO/Fe3O4 nano adsorbent (Fe3O4/C-nanodot/GO) showed superior working condition than GO/PANI. The validated proposed analytical methods can be used for the quantitative determination of methamphetamine and pseudoephedrine in actual samples.

Effect of Silver Nanoparticle Size on Antibacterial Activity

The ubiquitous use of products containing AgNPs results in the entry of nanoparticles into the environment. Both nanoparticles and Ag+ released upon their oxidative dissolution have a toxic effect on living microorganisms. The antibacterial activity of spherical silver nanoparticles of 10.8 ± 0.8 nm and 22.7 ± 2.2 nm in size stabilized by carbonate ions was studied against Escherichia coli and other bacteria. The biocidal action of silver increases as the particle size decreases. Analysis of these results and other known data made it possible to substantiate a linear proportional relationship between the minimum inhibitory concentration (MIC) or the half-maximal inhibitory concentration (IC50) and silver nanoparticle size and determine empirical parameters for this relationship. The antibacterial activity (toxicity) is directly proportional to the specific surface area of nanosized silver.

Exploring Multi-Parameter Effects on Iron Oxide Nanoparticle Synthesis by SAXS Analysis

Iron oxide nanoparticles (IONs) are extensively used in biomedical applications due to their unique magnetic properties. This study optimized ION synthesis via the co-precipitation method, exploring the impact of the reactant concentrations (Fe(II) and Fe(III)), NaOH concentration, temperature (30 °C–80 °C), stirring speed (0–1000 rpm), and dosing rate (10–600 s) on particle size and growth. Using small-angle X-ray scattering (SAXS), we observed, for example, that higher temperatures (e.g., 67 °C compared with 53 °C) led to a 50% increase in particle size, while the stirring speed and NaOH concentration also influenced nucleation and aggregation. These results provide comprehensive insights into optimizing synthetic conditions for targeted applications in biomedical fields, such as drug delivery and magnetic resonance imaging (MRI), where precise control over nanoparticle size and properties is crucial.

E. coli-Assisted Eco-Friendly Production of Biogenic Silver Cobalt Oxide (AgCoO2) Nanoparticles: Methanolysis-Based Hydrogen Production, Wastewater Remediation, and Pathogen Control.

Herein, bacterial-assisted synthesis of AgCoO2 is carried out. In the first step, E. coli was separated from soil samples via the "serial dilution method." Ten milliliters of bacterial supernatant was mixed with cobalt chloride and silver nitrate hatched at 38°C for 24 h to get AgCoO2 nanoparticles (NPs). XRD results confirm the synthesis of AgCoO2 NPs while EDX results confirm the absence of any other elements than Ag, Co, and O. An average NP size of 12-26 nm was determined by TEM examination, and the surface of the particles was seen rough, irregularly shaped borders. The antibacterial activity of the constructed NPs was checked against S. aureus, E.coli, Bacillus subtilus, and Pseudomanas areguinosa using agar well diffusion method. The maximum zone of inhibition was 27 mm at 40 mg/mL against Bacillus subtilus. The performance of the synthesized NPs as photocatalysts was also assessed, and several operational parameters that control the photodegradation of the harmful dyes were tried to tune as well, and 85% degrading efficiency was obtained at 60oC for 240 min for 30 mg of catalyst dose These NPs were also used to produce hydrogen by methanolysis.

Optimising the production of PLGA nanoparticles by combining design of experiment and machine learning

Poly(lactic-co-glycolic acid) (PLGA) is a widely used biodegradable polymer in drug delivery and nanoparticle (NP) formulation due to its controlled drug release properties and safety profiles. Among the methods available for NP production, nanoprecipitation is distinguished by its simplicity and scalability. However, it requires careful optimisation to achieve the desired NP characteristics, making the process potentially lengthy and costly. This study aimed to assess and compare the predictive performance of Design of Experiments (DOE) and Machine Learning (ML) models for the optimisation of PLGA nanoparticle size and zeta potential produced by nanoprecipitation. Various ML methods were employed to predict particle size, with Extreme Gradient Boosting (XGBoost) identified as the best performing. The key finding is that integrating ML with DOE provides deeper insights into the dataset than either method alone. While ML outperformed DOE in predictive performance, as evidenced by lower root mean squared error values and higher coefficients of determination, both methods struggled to accurately predict zeta potential, generating models with high errors. However, ML proved more effective in identifying the parameters that most significantly influence NP size, even with a smaller DOE dataset. Combining DOE datasets with ML for parameter importance was particularly advantageous in situations where data is limited, offering superior predictive power and the potential to streamline experimental design and optimisation. These results suggest that the synergistic use of ML and DOE can lead to more robust feature analysis and improved optimisation outcomes, particularly for NP size. This integrated approach can enhance the accuracy of predictions and supports more efficient experimental design, streamlining nanoparticle production processes, especially under resource-constrained conditions.

AI-assisted autonomous manufacturing of tailored drug-loaded nanoparticles by multi-step continuous-flow platform

In the development of highly effective drug delivery systems (DDS), autonomous multi-step manufacturing of tailored nanoparticles in size and drug loading was achieved by seamlessly integrating combinatorial synthesis of drug-loaded nanoparticles, rapid flow-dialysis to purify them, followed by real-time characterization of size and encapsulation efficiency (EE) in a continuous-flow manner. To accomplish this, a newly developed ultrafast flow-dialysis module with multiple buffer injectors for efficient removal of impurities within 10 min was utilized. These integrated multi-step flow processes were autonomously controlled by leveraging a multi-objective Bayesian optimization (MOBO) algorithm. Consequently, this artificial intelligence (AI)-assisted rapid screening of multi-step parameters accomplished the swift manufacturing of both Doxorubicin (DOX)-loaded liposomes and polymeric nanoparticles, customized in terms of size and drug loading within 6 h for each. This AI-based autonomous platform envisions accelerating the development timeline for bespoke DDS applications, addressing diverse therapeutic needs in the field of nanomedicine.

Preparation of salt-free Pleurotus eryngii protein with enhanced colloidal stability and emulsifying properties by ceramic membrane filtration

Fungi proteins represent a potential alternative to animal proteins, which are regarded as a prospective source of edible proteins due to more sustainable and efficient cultivation of fungi. Nevertheless, the extraction of fungi proteins typically involves alkali extraction combined with acid precipitation (AEAP), yielding considerable amounts of salt that causes aggregation and inferior functional properties of proteins. In this study, we put forward a novel extraction method by direct filtration of Na+ and OH− from the alkaline extraction solutions using ceramic membranes. This method enabled the continuous, efficient and cost-effective production of Pleurotus eryngii protein (PEP). The PEP prepared by this technology featured a uniform distribution of nanoparticles (∼129.5 nm) with higher water dispersity, colloidal stability and surface active properties as compared to that prepared by the AEAP method. Moreover, the microstructures of PEP can be tailored with various nanoparticle sizes and size distribution profiles, allowing for the adjustment of long-term stabilities and surface activities. Our study renovated the production technology of fungi proteins to enhance their functionalities as well as the application prospects in the future food industry.

Nanoparticle size: A critical role in enhancing oil recovery

In recent years, nanomaterials have shown great potential in the enhanced oil recovery (EOR) process due to their superior properties. However, the influence of particle size on EOR is not clear and hinders the application. Therefore, it is vitally important to investigate the effect of the particle size on the EOR performance. In this work, Fe3O4 nanoparticles with different sizes, including 20 nm, 100 nm, 200 nm, and 300 nm, were controllably synthesized and used to innovatively clarify the critical role of nanoparticles size in enhancing oil recovery. The effects of sizes on the recovery were investigated by combining the experimental results of interfacial tension, wetting angle, and spontaneous imbibition. The results revealed that Fe3O4 nanoparticles with smaller size, which have the stronger ability to reduce the interfacial tension and change the wetting angle, exhibit a favorable capacity to enhance oil recovery. Compared with the spontaneous imbibition oil recovery (24.36 %) of standard brine, the nanofluid containing 0.1 wt% Fe3O4 with the size of 20 nm can increase the recovery to 30.87 %. Based on the results of nuclear magnetic resonance, the spontaneous imbibition mechanism was proposed and the effect of size on recovery was analyzed. In addition, the superparamagnetism of Fe3O4 was utilized for recycling, avoiding the issue of significant losses of nanofluids in the existing research after injection. The secondary spontaneous imbibition was carried out, showing good environmental friendliness.

DDDNet: A lightweight and robust deep learning model for accurate segmentation and analysis of TEM images

The primary aim of this study was to develop an optimal, lightweight model for the segmentation of transmission electron microscopy (TEM) images. Our model is designed with a minimal parameter count, superior performance metrics, and robust adaptability to variations in substrates, nanoparticle sizes, and nanomaterial diversity within TEM images. In achieving this, we benchmarked our model against four deep learning models using subsets from the Bright-Field TEM(BF-TEM) and Au-TEM datasets. Our model demonstrated exceptional segmentation performance, requiring only 0.34 M parameters and 39.33 G floating-point operations. It also provided the most accurate estimates of average nanoparticle sizes, closely matching true labeled values. These results confirm the model’s proficiency and precision in TEM image processing and introduce a powerful tool for nanoscale image analysis. Our work sets a new standard for lightweight and efficient TEM segmentation models, paving the way for future advancements in nanotechnology research.