Conventional Analysis Methods Research Articles

Risk identification of integral pressurized water reactor (IPWR) cooling system using a combination HAZOP, FMEA, and FTA methods

AbstractIntegral Pressurized Water Reactor (IPWR) is gaining attention for use in Indonesia due to lower initial capital investment, sitting flexibility, and high level of inherent safety. IPWR relies heavily on passive cooling systems that have many advantages but lacks operational experience because no one operates commercially. Therefore, it is necessary to carry out a risk analysis related to passive cooling system failures that may occur during operation. Even if conventional risk analysis methods are applied singularly, the results still have shortcomings. This study aims to integrate the use of the Hazard and Operability Analysis (HAZOP), Fault Tree Analysis (FTA), and Failure Mode and Effect Analysis (FMEA) methods to identify risks in the IPWR primary cooling system during normal operation. HAZOP was used to identify hazards, FTA analyzed the root causes of the critical problems, and the basic event's FMEA was developed for risk ranking and mitigation measures. This combined methodology makes risk assessment more systematic and comprehensive because it manages entity‐wide risk. The results offer valuable insights for stakeholders involved in decision‐making regarding the development of nuclear power plants (NPPs) in Indonesia.

A simple and efficient alginate hydrogel combined with surface-enhanced Raman spectroscopy for quantitative analysis of sodium nitrite in meat products.

Sodium nitrite is a commonly used preservative and color protectant in the food industry. Conventional analytical methods are highly susceptible to food matrix interference, time-consuming and costly. In this study, the ion cross-linking method was employed to prepare alginate hydrogel substrates, and phenosafranin was chosen as a single-molecule probe to analyze sodium nitrite. Our investigation centered on elucidating the effects of alginate and cross-linking ion concentrations on Raman signal characteristics. The optimal Raman response was observed in the precursor solution with 1% sodium alginate and 0.1 mol L-1 cross-linking ions. The relative standard deviations (RSDs) of the feature peaks from the three substrate batches ranged from 1.22% to 16.30%, attesting the robustness and consistency of the substrates. The signal reduction of the substrates after a four-week storage period remained below 10%, indicating that the substrates had good reproducibility and stability. The limits of detection (LODs) for sodium nitrite in extracts from cured meat, luncheon meat, and sliced ham were determined to range from 3.75 mg kg-1 to 8.11 mg kg-1, with low interference from the food matrix. The support vector machine algorithm was utilized to train and predict the data, which proved to be more accurate (98.6%-99.8% recovery) than the traditional linear regression model (81.9%-112.7% recovery) in predicting the spiked samples. The application of hydrogel-based surface-enhanced Raman spectroscopy (SERS) substrates for nitrite detection in food, combined with machine learning for regression prediction in data processing, collectively augmented the potential of SERS technology in the field of food analysis.

RNA-Seq Data Analysis.

RNA-Seq data analysis stands as a vital part of genomics research, turning vast and complex datasets into meaningful biological insights. It is a field marked by rapid evolution and ongoing innovation, necessitating a thorough understanding for anyone seeking to unlock the potential of RNA-Seq data. In this chapter, we describe the intricate landscape of RNA-seq data analysis, elucidating a comprehensive pipeline that navigates through the entirety of this complex process. Beginning with quality control, the chapter underscores the paramount importance of ensuring the integrity of RNA-seq data, as it lays the groundwork for subsequent analyses. Preprocessing is then addressed, where the raw sequence data undergoes necessary modifications and enhancements, setting the stage for the alignment phase. This phase involves mapping the processed sequences to a reference genome, a step pivotal for decoding the origins and functions of these sequences.Venturing into the heart of RNA-seq analysis, the chapter then explores differential expression analysis-the process of identifying genes that exhibit varying expression levels across different conditions or sample groups. Recognizing the biological context of these differentially expressed genes is pivotal; hence, the chapter transitions into functional analysis. Here, methods andtools like Gene Ontology and pathway analyses help contextualize the roles and interactions of the identified genes within broader biological frameworks. However, the chapter does not stop at conventional analysis methods. Embracing the evolving paradigms of data science, it delves into machine learning applications for RNA-seq data, introducing advanced techniques in dimension reduction and both unsupervised and supervised learning. These approaches allow for patterns and relationships to be discerned in the data that might be imperceptible through traditional methods.

Comparison between Microwave Digestion and Conventional Open Digestion Method for Heavy Metals determination

In the present study a comparison between microwave assisted digestion and conventional wet acid open digestion system for dissolution of bivalve tissue samples was carried out. Sample matrix decomposition was done by using a mixture of concentrated nitric acid and hydrogen peroxide. The determination of trace elements (Zn, Co, Cu, Cd and Pb) in commercially popular bivalve species from two important fish landing centers in Mumbai was used for this purpose. The recovery of heavy metals after analysis was done using certified reference material TORT 2- Lobster hepatopancreas for both the digestion methods. The results of the present study indicate that microwave digestion system for heavy metal analysis is the preferred method for rapid and good quality results. It provides better recovery and precision for heavy metal analysis. The sample preparation step is much easier by microwave digestion system compared to conventional open digestion methods for heavy metal analysis. It is particularly ideal for trace metals like lead and cadmium as they are generally present in very small concentration in biological samples. In the current study microwave digestion proved to be better method than conventional open digestion method with rapid sample digestion process and least sample contamination problem.

Medical Professionals' Responses to a Patient Safety Incident in Healthcare.

Objectives: Patient safety incidents (PSIs) are common in healthcare. Open communication facilitated by psychological safety in healthcare could contribute to the prevention of PSIs and enhance patient safety. The aim of the study was to explore medical professionals' responses to a PSI in relation to psychological safety in Slovak healthcare. Methods: Sixteen individual semi-structured interviews with Slovak medical professionals were performed. Obtained qualitative data were transcribed verbatim and analysed using the conventional content analysis method and the consensual qualitative research method. Results: We identified eight responses to a PSI from medical professionals themselves as well as their colleagues, many of which were active and with regard to ensuring patient safety (e.g., notification), but some of them were passive and ultimately threatening patients' safety (e.g., silence). Five superiors' responses to the PSI were identified, both positive (e.g., supportive) and negative (e.g., exaggerated, sharp). Conclusion: Medical professionals' responses to a PSI are diverse, indicating a potential for enhancing psychological safety in healthcare.

Development of a Premarital Education Package Based on Transactional Analysis Theory

Objective: The aim of the present study was to develop a premarital education package based on Transactional Analysis theory. Methods and Materials: The scope of this research encompassed all texts related to the psychological theory of Transactional Analysis. The selection of texts was purposefully done from Eric Berne's theoretical works within the timeframe of 1990 to 2024. The criterion for the number of texts was based on the principle of data saturation. The method of data collection involved line-by-line examination of texts, guided by questions relevant to the components suitable for premarital counseling in this theory. The data obtained from the text reviews were analyzed using the conventional content analysis method by Hsieh and Shannon (2005). Findings: The results of the content analysis indicated that in Eric Berne's theory, 1) the ego states (Adult, Parent, and Child), life script (characteristics of the life script, elements of the life script, and four life positions), and transactions (complementary transactions, crossed transactions, ulterior transactions, and games) are appropriate for alignment with premarital education. Subsequently, these concepts were integrated theoretically using the method by Yousefi and Golparvar (2023), and ultimately, the premarital education package was designed and prepared. Conclusion: Overall, considering the process of deriving this education, it can be said that it is content-valid and can be evaluated at the level of designing an educational package and conducting experimental reviews on marriage-related constructs.

CNN-LSTM Hybrid Model for Enhanced Malware Analysis and Detection

The ever-evolving tactics employed by malware authors to avoid detection pose challenges to the conventional static analysis method, which entails examining the malware code. These challenges arise from the authors’ capacity to obfuscate their code. To address this matter and enhance the identification of malware, integrating dynamic detection and machine learning has emerged as a highly promising approach. This methodology has demonstrated efficacy in identifying malware specifically engineered to circumvent established detection techniques. Behavioural analysis is a crucial component in ensuring endpoints’ security, with the CNN-LSTM algorithm being particularly notable for its effectiveness in identifying Zero-Day malware. This type of malware poses a substantial obstacle to conventional signature-based approaches. This paper aims to assess the efficacy of the Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model, emphasising its significance in tackling the continuously evolving realm of cybersecurity obstacles. The research highlights the significance of transitioning from traditional signature-based detection methods to behavioural analysis techniques. It suggests utilising deep learning approaches such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) to improve the ability to detect malware in an environment where threats constantly evolve. The malware detection system that has been developed encompasses a log parser analyser, API monitoring, and an extension checker module. The CNN-LSTM model demonstrates a commendable ability to accurately identify malicious behaviour, achieving a validation accuracy of 96%. This study demonstrates the efficacy of employing behavioural analysis and deep learning techniques to enhance cybersecurity, particularly in addressing sophisticated, evasive, and previously unknown malware risks.

The concept of succession evasion: An invisible behavior in the organization.

The opposite of succession planning is a new concept called succession evasion; A deep understanding of this concept helps to correct the training process of competent staff in nursing management. This study was conducted with the aim of explaining succession evasion as a new concept in nursing management; Also, as an invisible aspect of organizational behavior in the field of management. The current research was conducted using a qualitative approach and conventional content analysis methods. The participants included nursing managers selected using the purposeful sampling method based on the entry and exit criteria. The code of research ethics and required permits were received. The method of semi-structured interviews was used to collect data, and the time of the interviews varied between 39 and 90 minutes. All data was recorded and transcribed. The method proposed by Graneheim and Lundman was used for data analysis. Guba and Lincoln's criteria were used for the accuracy and robustness of the data. MAXQDA software was used for data management. The subcategories related to succession planning included "Fear of succession planning," "Talent suppression," "Belief in the permanence of the position," and "Obstructing the growth of others." Succession evasion as an undesirable organizational behavior had four primary categories, fear of succession planning, talent suppression, belief in-Popsition stability, and obstruction to the growth of others. It is recommended to use the findings of this study in the context of further explanation of the concept of succession evasion as the opposite of succession planning in future research, and also, to use this concept for planning nursing management.

An Iterative Equal Area Criterion for Transient Stability Analysis of Grid-Tied Converter Systems With Varying Damping

With the increasing penetration of renewable energy generators, the stability issues of grid-tied converter systems become much more important. However, due to the high nonlinearity and varying damping of converter systems, conventional transient stability analysis methods are not applicable, which may bring to conservativeness or misjudgment on stability assessment. In this paper, the transient stability of grid-tied converter systems with varying damping is investigated to provide stability boundary estimation. Firstly, considering the frequency mutation of the phase-locked loop (PLL) caused by various perturbations, a modified swing equation model of grid-tied converter systems is built, which greatly improves the mathematical model accuracy under transient disturbances. To evaluate the impacts of varying damping and frequency mutation, an iterative equal area criterion (ITEAC) method is proposed with the iterative calculation of the accelerating and decelerating area, which renders stability boundaries with high accuracy. Moreover, the impacts of controller and system parameters on stability boundaries are quantitatively analyzed. Eventually, simulation and hardware-in-loop experiments are performed to verify the effectiveness and superiority of the proposed ITEAC.

Factors Affecting the Quality of Sexual Life in Iranian Elderly Men With Andropause

Objectives Andropause is the common term for menopause in older men, which is characterized by a decrease in testosterone levels. Many elderly men with andropause have difficulty or are embarrassed to talk about sexual topics, which causes a lack of information or wrong beliefs and attitudes towards this issue, especially among families. The existing studies in Iran have not had a deep and special attention on this issue. Therefore, this study aims to qualitatively investigate the factors affecting the quality of sexual life in Iranian elderly men with andropause. Methods & Materials This is a qualitative study conducted in 2022 using the content analysis method. Using a purposeful sampling method, 14 elderly men with andropause were selected from among those who visited five health centers in different areas of Mashhad city in Iran. Data were collected using a semi-structured interview. Data analysis was done at the same time as sampling by using the conventional content analysis method. MAXQD software was used for data management. Results The mean age of the participants was 70 years. Eight men had primary education, three had middle school education, one had a high school diploma, and two were illiterate. The findings included 290 codes, 19 subcategories, 9 categories, and 3 themes. The themes were perceived sexual impotence, non-sexual feelings and experiences, and the desire to have an active sexual life. Conclusion The result of in-depth interviews with the elderly with andropause showed the components that can be addressed by officials and researchers for facilitating appropriate planning in this area. The knowledge of the existing situation and how the affecting factors can be the basis of new studioes in this field.

Teachers and managers experiences of virtual learning during the COVID-19 pandemic: A qualitative study

The closure of universities and schools interrupted learning outcomes and deprived students from growth and development opportunities as well as hindering their academic progress. Indeed, the COVID-19 Pandemic changed educational mechanism from traditional method to staying at home and virtual education globally. On the other hand, the educators and student particularly in developing countries faced shortage of necessary software and hardware infrastructures. Accordingly, introduction of virtual education along with some organizational and regional obstacles as well as poor planning caused low-quality online courses. Therefore, the present study extrapolated teachers' virtual education experiences during the COVID-19 pandemic. This qualitative study conducted on Iranian teachers using the conventional content analysis method in 2022. To do this, the targeted sampling method with maximum variation continued until data saturation. Hence, a total of 17 teachers (10 women and 7 men) included in the study. Data collected through five focus group discussions on the Sky room platform and two individual in-depth interviews. Male teachers had an average age of 44.42 years, while female ones had an average age of 47.80 years. The qualitative analysis categorized 22 themes as virtual education's pros and cons. Mental, physical, and social injuries; economic problems; insufficient virtual teaching skills; lack of virtual infrastructure; lack of motivation were identified as the main disadvantages of virtual education. Virtual education benefits included familiarity with modern science and education, time management, the durability of video and course topics, and not forcing students to attend class. Removing structural barriers (like creating a good platform for practical education and stopping new restrictive policies) and individual barriers (like economic and family problems, lack of motivation, psychological pressures, and occupational stress) could possibly improve virtual education. Virtual education should be different from face-to-face teaching because it can only meet some students' needs.

Strong cation-exchange combined with mass spectrometry reveals the glycoform heterogeneity of sialylated glycoproteins.

Sialylation is an important modification of proteins, related to protein life and bioactivity. However, the evaluation of sialylation is only based on the average molecular composition by peptide mapping and glycan profiling because sialylated proteins are usually too heterogeneous to obtain good quality mass spectra by conventional intact mass analysis methods. In this study, a simple strong cation exchange-mass spectroscopy (SCX-MS) method was developed for intact mass analysis of sialylated glycoproteins. The developed SCX-MS method provided good separation for sialylated glycoproteins and had an inherent characteristic of native MS. Thus, the intact mass analysis of highly heterogeneous glycoprotein, which cannot be obtained by reversed-phase liquid chromatography (RPLC)-MS and size exclusion chromatography (SEC)-MS methods, can be well analyzed using the current SCX-MS method. First, the method was developed and optimized using the etanercept monomer. Conditions including MS parameters, flow rate, and gradient were investigated. Then, the developed method was used to analyze a new recombinant vaccine, protein 1. Similar to the etanercept monomer, the intact molecular information of protein 1, which cannot be obtained by RPLC-MS and SEC-MS, can be achieved using SCX-MS. Combined with information obtained on peptide mapping and glycan profiles obtained by LC-MS, the new vaccine was well characterized. Finally, the SCX-MS method was used to quickly evaluate the batch-to-batch reproducibility of protein 1. It was much faster than peptide mapping and glycan profiling methods and can provide information complementary to these strategies. It should be useful for many applications where speed and comprehensive characterization are required, such as recombinant sialylated vaccines and fusion proteins.

Direct detection of dithiocarbamate fungicides by SALDI/MS using porous TiC ceramic powder as a substrate.

Dithiocarbamate fungicides (DTCs) have been widely used all over the world. Some of them show toxicities, such as growth toxicity and teratogenicity. Therefore, the analysis of DTCs in environments or crops is very significant. However, their direct and individual analysis was difficult, because most of them are metal complex compounds and have macromolecular properties and a low solubility in water or organic solvents. In the conventional analytical methods for DTCs, the total amount of DTCs was obtained by the quantification of the derivatives of the ligand or by measuring the carbon disulfide formed by the decomposition of the fungicides. Surface assisted laser desorption (SALDI)/MS can detect various compounds, such as metal complexes and macromolecules, present in a nanostructured substrate. The porous titanium carbide (TiC) ceramic powder shows adsorptive properties to various substances and can be used as a substrate for SALDI/MS. In this study, a method for the individual and direct detection of dithiocarbamate pesticides by SALDI/MS using porous TiC ceramics as a substrate has been developed. The dithiocarbamate fungicide was mixed with the porous TiC powder in a mortar, and the mixture was analyzed by SALDI/MS. The deprotonated ion of the ethylene-bis-dithiocarbamate complex, mancozeb or zineb, was detected in the negative ion mode. For the dimethyldithiocarbamate complexes, ferbam and ziram, the ion of the eliminated dithiocarbamate ligand was detected in the positive ion mode. Calibration curves by the present method for Manzeb showed good linearity by using an internal standard material. Based on these results, we concluded that this method is useful for the analysis of DTCs.

Recent progress in electrochemical detection of amaranth in food samples: A brief review

Amaranth (E 123, AM) is a red synthetic azo dye authorized as a food additive in the EU. In contrast to natural colorants, AM has significant advantages such as low production cost, excellent water solubility, and high stability. AM is used to enhance the natural color, to decrease the loss of color due to processing treatments, and to provide color to colorless ultra-processed foods, making food more attractive to the consumers. AM is widely used in food industry to improve the appearance and color of different types of food and beverages, including confectionery products, jelly, candies, jams, dairy products, ice creams, sausages, wines, syrups and soft drinks. Many of these food products are manufactured directly for, and marketed directly to, children. The high amount of AM in food products can cause adverse health effects such as dizziness, allergies, and respiratory problems. Moreover, long-term studies indicated that the potential adverse effects induced by AM include genotoxicity and cytotoxicity. Therefore, the development of a precise and easy-to-handle alternative for a quick and efficient quantitative detection of AM in food products is necessary for consumer safety. Modern electroanalytical techniques have the advantages of excellent sensitivity and selectivity, instrumental simplicity and portability, providing reliable alternatives to the conventional analytical methods. In this brief review, we have summarized recent trends in the electrochemical sensor systems applied for the analysis of AM in food samples, critically evaluated the performance metrics of these sensors: sensitivity, linear range, limit of detection and stability. Finally, the current challenges and future prospects are outlined.

Methylation-Specific Electrophoresis.

Methylation of CpG sites in the promoter region of genomic DNA is an important epigenetic modification that plays a critical role in gene regulation, particularly in gene silencing. Epigenetic abnormalities, along with genetic alterations, are implicated in carcinogenesis and cancer progression. Numerous studies have investigated the role of epigenetics in cancer using various tools to assess DNA methylation. However, conventional analysis methods for DNA methylation require a large amount of DNA but lack higher sensitivity, making them unsuitable for analysis of samples with high heterogeneity, such as tumor tissues. In this study, we introduce a novel electrophoresis method named "methylation-specific electrophoresis (MSE)," which utilizes a denaturing gradient acrylamide gel. We demonstrate the applicability of the MSE method for DNA methylation analysis of the mucin gene as an example.

Risk Assessment Method for Analyzing Borehole Instability Considering Formation Heterogeneity

In the study of borehole instability, the majority of input parameters often rely on the average values that are treated as fixed values. However, in practical engineering scenarios, these input parameters are often accompanied by a high degree of uncertainty. To address this limitation, this paper establishes a borehole stability model considering the uncertainty of input parameters, adopts the Monte Carlo method to calculate the borehole stability reliability at different drilling fluid densities, evaluates the sensitivity of borehole instability to a single parameter, and studies the safe drilling fluid density window at different borehole stability reliability values under multi-parameter uncertainties. The results show that the uncertainty of rock cohesion has a great influence on the fracture pressure of the vertical and horizontal wells. The minimum horizontal stress has the greatest influence on the fracture pressure of the vertical and horizontal wells, followed by pore pressure. In the analysis of borehole stability, the accuracy of cohesion and minimum horizontal stress parameters should be improved. In scenarios involving multiple parameter uncertainties, while the overall trend of the analysis results remains consistent with the conventional borehole stability outcomes, there is a noteworthy narrowing of the safe drilling fluid density window. This suggests that relying on conventional borehole stability analysis methods for designing the safe drilling fluid density window can considerably increase the risks of borehole instability. Uncertainty assessment is crucial to determine the uncertainties associated with the minimum required mud pressure, thereby ensuring more informed decision-making during drilling operations. To meet practical application demands, structure and boundary condition uncertainties should be implemented for a more comprehensive assessment of borehole stability.

Explaining Midwifery Students’ Experience of Their First Attendance in Clinical Skill Centers: A Content Analysis Study

Background: Learning clinical skills on “real patients” not only endangers patient safety but also raises many ethical concerns. Methods: This is a qualitative study designed and implemented using the content analysis method. The studied population included all midwifery students of Ahvaz Jundishapur University of Medical Sciences who had recently passed the course: “Introduction to the Principles and Techniques of Patient Care at Clinical Skill Centers (CSC)”. Data were analyzed by conventional content analysis method. Results: Overall, 156 codes were extracted from the research data, which were organized into 9 categories and 28 subcategories after removing or merging similar codes. The midwifery students’ experience of attending CSC was reflected in the form of three main themes (i.e., self-regulation, mentoring, and support management) and nine sub-themes (reinforcing intrinsic motivation in students, building self-confidence in students, mentor as a model, proper formulation and implementation of rules in CSC, training communication skills, the need to apply modern training methods, revising the structure, practice, and schedule of student groups). Conclusions: According to the findings of the present study, it could be concluded that primary education in CSC plays a very important role in reducing the stress of working with patients, professionalism, increasing self-confidence, and teaching clinical skills to students. Of course, achievement of this goal heavily relies on the crucial role of teachers of this course, who act as mentors responsible for planning, organizing, and modeling in addition to teaching.

Simplified capture, extraction, and amplification of cellular DNA from water samples.

DNA analysis in water samples is attracting attention in various fields. However, conventional methods for DNA analysis require a work-intensive and time-consuming sample pre-treatment. In this study, a simplified pre-treatment method for analyzing DNA in water samples was evaluated. The process consists of filtration, DNA extraction, and amplification, which can be achieved within a short time. In the filtration process, two types of filters, firstly a tissue paper (Kimwipe) and then a glass filter (GF/F), were used in sequence. The first large pore size filter enabled a reduction in filtration time by removing large particulate matter impurities present in river water matrix. Cells spiked into 1 L of river water were recovered at more than 90% within approximately 5min filtration time. Also, DNA was extracted from the captured cells directly on the surface of the filter in only 5min. Thus, DNA collection and extraction from a water sample can be completed within about 10min. Furthermore, PCR amplification was performed directly from DNA-attached filter sections, which greatly reduced the number of required pre-treatment steps. Finally, we succeeded in establishing a simple and fast on-site pre-treatment system by using a hand-driven syringe filtration method. This pre-treatment system is expected to offer the possibility for the future establishment of a rapid and easy DNA analysis method applicable to various types of water samples.

Environmental DNA in a Biofilm Can Be Used to Assess Diatom Ecological Health in Stream Water Ecology

In urban and agricultural streams, assessing aquatic ecosystem health is critical due to widespread pollution. Traditional methods for evaluating attached diatoms crucial for ecosystem monitoring face limitations such as species misidentification and sample damage. This study was conducted in the Miho River within the Geum River system and highlights the effectiveness of environmental DNA (eDNA) techniques for more accurate and efficient genetic-based analysis than conventional microscopic analysis methods. When eDNA-based assessments were compared with traditional microscopic methods, this study found that eDNA analysis often revealed poorer ecosystem health. Notably, eDNA assessments showed a stronger correlation with phosphorus concentrations, underlining their precision and importance in ecological studies. These findings suggest that eDNA has potential as a valuable tool for comprehensive biomonitoring. However, the use of international genetic barcode databases in eDNA analysis could lead to the identification of unrecorded species in Korea. Therefore, this study recommends developing a localized genetic barcode database and constructing eDNA information through meta-barcoding, focusing on native species. This approach is crucial for enhancing the accuracy and applicability of eDNA-based health assessments in national biomonitoring efforts.

Measurement of number-weighted particle size distribution for CMP slurry using nanoparticle chip

Chemical mechanical polishing/planarization (CMP) slurry contains mainly abrasive grains (primary particles) with few aggregations (secondary particles). Measuring the particle size distribution (PSD) of CMP slurry is crucial for improving the productivity of the CMP process. For the quality management of CMP slurry, it is necessary to evaluate both the sizes and quantities of both the primary and secondary particles. Conventional PSD analysis methods, except for image analysis, face challenges in identifying primary and secondary particles for PSD measurements. For image analysis, the particles must be transferred from a suspension to a substrate using the conventional sampling method; however, this creates aggregates, resulting in a change in the PSD of the particles on the substrate compared to that in suspension under poly-dispersed conditions. Thus, this study proposed a novel particle sizing method using nanoparticle chip (NPC) to assist in the image analysis of the PSD. The NPC can pick up a single particle in a small volume droplet to avoid aggregation and maintain the poly-dispersed condition of the particles in suspension. The primary and secondary particles can be identified using scanning electron microscope (SEM) or atomic force microscope (AFM), and the PSD can be evaluated by measuring the area and height of the particles. Further, the quantities of both the primary and secondary particles can be counted from the substrate. This study presented a comparison of the diameter measurements using NPC and the conventional method. The results show that the NPC identified the primary and secondary particles and decreased the measurement error of the particle diameters. Consequently, the NPC proves to be superior to the conventional method for use in the PSD analysis of CMP slurry.