https://pubs.kist.re.kr/handle/201004/153343 Wed, 08 Apr 2026 20:41:51 GMT 2026-04-08T20:41:51Z https://pubs.kist.re.kr/handle/201004/154549 Title: Flow Plastometry of Microplastics Using Optical Line Tweezers Authors: Jee Won Lee; Park, Subeen; Jin Il Jang; KIM, JAE HUN; Hyung Min Kim Abstract: Microplastics (MPs), both primary and secondary, have been reported to cause adverse effects on ecosystems, including human health. A substantial portion of MPs has been identified in aquatic environments such as oceans, rivers, lakes, reservoirs, and even drinking water systems. The potential human health risks of MPs absorbed through water intake have been increasingly reported. The chemical composition, which includes core components and surface coatings, as well as physical characteristics such as size and shape, collectively determines their toxicity. However, preparation steps such as density separation, digestion, and filtration can chemically modify microplastics and cause matrix interference, necessitating the development of advanced analytical methods. This study introduces a novel system employing Raman line monitoring and trapping system to achieve simultaneous morphological and chemical analysis of MPs in a flow channel. Furthermore, we developed a protocol for acquiring Raman signals from particles as small as 500 nm through precise optical immobilization. Our approach can provide a powerful tool for real-time monitoring and characterization of MPs in aquatic environments. Sun, 01 Mar 2026 00:00:00 GMT https://pubs.kist.re.kr/handle/201004/154549 2026-03-01T00:00:00Z https://pubs.kist.re.kr/handle/201004/154548 Title: Decoding chemical composition of urinary crystals from ultrasonic echo signals via deep learning Authors: Sridharan, Badrinathan; Salman, Maaz; Park, Yeong Seo; Lee, Eun Ju; Tak, Soonhyuk; Ryu, Jegyeong; Oh, Junghwan; Lee, Deuk hee; Lim, Hae Gyun Abstract: Conventional analyses of urinary stones like estimation of urinary markers provide ambiguous predictions on the type of stone while XRD, and FT-IR can only be performed on surgically removed samples. Hence, current clinical practice lacks non-invasive methods for early and accurate classification of stones based on their composition. This study presents an ultrasound-based non-invasive system integrated with a machine-learning algorithm for analyzing acoustic signals to classify in vitro fabricated urinary crystals. Calcium oxalate, calcium phosphate, and uric acid crystals were synthesized, characterized, and embedded in gelatin-based gel phantoms. Following morphological characterization of the crystal-infused phantoms, ultrasonic echo signals were acquired and processed using machine learning-based analytical models. The results demonstrated that the ultrasound-based system effectively enables non-invasive analysis of urinary stones and can provide information on their location and structural characteristics. In parallel, the machine-learning component enhances diagnostic accuracy by classifying the stone composition from the analyzed acoustic signals, facilitating early and precise diagnosis. This approach highlights the potential of ultrasound and AI integration as a reliable diagnostic tool for personalized stone-management strategies. Sun, 01 Mar 2026 00:00:00 GMT https://pubs.kist.re.kr/handle/201004/154548 2026-03-01T00:00:00Z https://pubs.kist.re.kr/handle/201004/154547 Title: AI-powered computer interface using evoked potentials for XR biometric authentication and individual neural profiling Authors: Jeong Siwoo; Ko Jonghyeon; Park Sangin; Ha, Jihyeon; Chae Min Seong; Kim, Lae hyun; Mun Sungchul Abstract: Most authentication models are vulnerable to security breaches when personal data is exposed. This study introduces a novel hybrid visual computer interface integrating event-related potentials (ERPs) and steady-state visually evoked potentials (SSVEPs) to develop an authentication system that enhances both performance and personalization in neural interfaces. Our model utilizes distinctive neural patterns elicited by a range of visual stimuli based on 4-digit numbers, such as familiar numbers (personal birthdates, excluding targets), standard targets, and non-targets. The results revealed a distinct P300 response to familiar numbers when compared to both non-target and target stimuli. Incorporating these stimuli into our Transformer-based authentication system, coupled with personalized electroencephalogram (EEG) data segmentation, resulted in high accuracy in authenticating users and demonstrated remarkable robustness against security breaches. Additionally, a 10 Hz grow/shrink background image successfully elicited SSVEP. Furthermore, the comparison of harmonic and fundamental frequencies aids in optimizing neural interfaces. Wed, 01 Apr 2026 00:00:00 GMT https://pubs.kist.re.kr/handle/201004/154547 2026-04-01T00:00:00Z https://pubs.kist.re.kr/handle/201004/154545 Title: Functionalization-Driven Control of Flexural Acoustic Modes and Thermal Expansion in 2D BN Heterostructures Authors: Hossain Sk Mujaffar; Kim, Dobin; Park, Jaehyun; Lee Seung-Cheol; Bhattacharjee Satadeep Abstract: Two-dimensional (2D) materials often display negative thermal expansion (NTE) at low temperatures due to flexural acoustic modes. Here we combine density functional perturbation theory (DFPT) with the quasi-harmonic approximation (QHA) to quantify how chemical functionalization of monolayer hexagonal boron nitride (h-BN) modifies its thermal expansion. Using ab initio phonons and QHA, we computed the linear thermal expansion coefficient (LTEC) for pristine h-BN, a graphitic boron–nitride–carbon alloy (h-CBN), and a carbon/oxygen functionalized BN (f-BN) identified by systematic small-cell evaluation of distinct substitutional configurations. We find that functionalization both preserves dynamical stability (no imaginary phonons) and engineers the flexural branch: f-BN exhibits a more linear out-of-plane acoustic (ZA) dispersion near Γ relative to h-BN. As a result, f-BN reduces the magnitude of NTE by ≈34% compared to pristine h-BN in 0–300 K (minima in this range: h-BN −6.5 × 10–6 K–1, f-BN −2.6 × 10–6 K–1, h-CBN −1.05 × 10–6 K–1, graphene −4.23 × 10–6 K–1), while retaining NTE up to 1000 K. We attribute this quantitative tuning to a partial linearization of the flexural (ZA) branch near Γ and a redistribution of negative mode Grüneisen parameters toward higher frequencies. These results clarify the microscopic origin of NTE control in functional BN and suggest a practical route to mitigate thermal-mismatch strain for BN-based heterostructures and device interfaces. Sun, 01 Mar 2026 00:00:00 GMT https://pubs.kist.re.kr/handle/201004/154545 2026-03-01T00:00:00Z