Enhanced thermoelectric performance of copper iodide particles/nanowires composite in the low-temperature range.

Thermoelectric (TE) energy harvesting presents a viable method for reducing energy waste by transforming waste thermal energy into electricity. In this study, we fabricated copper iodide (CuI) composites using synthesized CuI nanowires (NWs) and particles to enhance TE performance in the low-temperature range. The Seebeck coefficient (S) was notably higher when a combination of CuI particles and NWs was used, reaching a maximum S of 1614.24 µV K-1 with a 60% NWs content at RT. Electrical conductivity (σ) exhibited an inverse correlation with S, with higher values detected when either particles or NWs were used only. The highest power factor (PF) of 128.44 µW m-1K-2 was recorded at RT with 60% NWs content, demonstrating improved TE performance. Thermal conductivity (κ) diminished when different material structures were employed, enhancing phonon scattering. The maximum figure of merit (ZT) achieved was ∼0.14 with 60% NWs content at 425 K, indicating the potential of this method for improving TE performance. This study offers valuable insights into optimizing TE performance using CuI composites, proposing a promising strategy for energy harvesting from low-temperature sources.

Improving the accuracy of noninvasive prenatal testing through size-selection between fetal and maternal cfDNA.

OBJECTIVES: In general, fetal cfDNA is shorter than maternal cfDNA, and accuracy of noninvasive prenatal testing (NIPT) results can be improved by selecting shorter cfDNA fragments to enrich fetal-derived cfDNA. This study investigated potential improvements in the accuracy of NIPT by performing classification and analysis based on differences in cfDNA size. METHODS: We performed paired-end sequencing to identify size ranges of fetal and maternal cfDNA from 62,374 pregnant women. We then developed a size-selection method to isolate and analyze both fetal and maternal cfDNA, defining fetal-derived cfDNA as less than 150 bp and maternal-derived cfDNA as greater than 180 bp. RESULTS: By implementing size-selection method, the accuracy of NIPT was improved, resulting in an increase in the overall positive predictive value for all aneuploidies from 89.57% to 97.1%. This was achieved by enriching both fetal and maternal-derived cfDNA, which increased fetal DNA fraction while the number of false positives for all aneuploidies was reduced by more than 70%. CONCLUSIONS: We identified the differences in read length between fetal and maternal-derived cfDNA, and selectively enriched both shorter and longer cfDNA fragments for subsequent analysis. Our approach can increase the detection accuracy of NIPT for detecting fetal aneuploidies and reduce the number of false positives caused by maternal chromosomal abnormalities.

Highly porous thermoelectric composites with high figure of merit and low thermal conductivity from solution-synthesized porous Bi2Si2Te6 nanosheets.

Layer-structured Bi2Si2Te6 has garnered significant attention in the field of thermoelectrics due to its exceptional thermoelectric properties and unique structural characteristics. Enhancing the transport properties of composites by manipulating the thermal and electrical properties of materials through the fabrication of porous nanostructured materials has emerged as a promising strategy. This paper presents a study on enhancing the thermoelectric (TE) properties of Bi2Si2Te6 nanosheets (BST NSs) through nanostructuring and the fabrication of porous BST NSs (p-BST). The process involves Li intercalation and exfoliation to obtain BST NSs, followed by the creation of p-BST composites by introducing nanosized pores onto the surface of the NSs using high-power sonification for various durations. The incorporation of the porous structure effectively increases phonon scattering, leading to a decrease in the lattice thermal conductivity (κl) of the composite. The p-BST(2) composite demonstrates significantly low κ and enhanced thermoelectric figure of merit (ZT) values (∼0.63 W m-1 K-1 and ∼0.083) at room temperature. These results highlight the efficacy of porous structure preparation as a promising strategy for enhancing the thermoelectric performance of chalcogenide-based composites, offering potential solutions to environmental degradation and energy shortages.

Enhancing Lung Cancer Classification through Integration of Liquid Biopsy Multi-Omics Data with Machine Learning Techniques.

Early detection of lung cancer is crucial for patient survival and treatment. Recent advancements in next-generation sequencing (NGS) analysis enable cell-free DNA (cfDNA) liquid biopsy to detect changes, like chromosomal rearrangements, somatic mutations, and copy number variations (CNVs), in cancer. Machine learning (ML) analysis using cancer markers is a highly promising tool for identifying patterns and anomalies in cancers, making the development of ML-based analysis methods essential. We collected blood samples from 92 lung cancer patients and 80 healthy individuals to analyze the distinction between them. The detection of lung cancer markers Cyfra21 and carcinoembryonic antigen (CEA) in blood revealed significant differences between patients and controls. We performed machine learning analysis to obtain AUC values via Adaptive Boosting (AdaBoost), Multi-Layer Perceptron (MLP), and Logistic Regression (LR) using cancer markers, cfDNA concentrations, and CNV screening. Furthermore, combining the analysis of all multi-omics data for ML showed higher AUC values compared with analyzing each element separately, suggesting the potential for a highly accurate diagnosis of cancer. Overall, our results from ML analysis using multi-omics data obtained from blood demonstrate a remarkable ability of the model to distinguish between lung cancer and healthy individuals, highlighting the potential for a diagnostic model against lung cancer.

Advances in methylation analysis of liquid biopsy in early cancer detection of colorectal and lung cancer.

Methylation patterns in cell-free DNA (cfDNA) have emerged as a promising genomic feature for detecting the presence of cancer and determining its origin. The purpose of this study was to evaluate the diagnostic performance of methylation-sensitive restriction enzyme digestion followed by sequencing (MRE-Seq) using cfDNA, and to investigate the cancer signal origin (CSO) of the cancer using a deep neural network (DNN) analyses for liquid biopsy of colorectal and lung cancer. We developed a selective MRE-Seq method with DNN learning-based prediction model using demethylated-sequence-depth patterns from 63,266 CpG sites using SacII enzyme digestion. A total of 191 patients with stage I-IV cancers (95 lung cancers and 96 colorectal cancers) and 126 noncancer participants were enrolled in this study. Our study showed an area under the receiver operating characteristic curve (AUC) of 0.978 with a sensitivity of 78.1% for colorectal cancer, and an AUC of 0.956 with a sensitivity of 66.3% for lung cancer, both at a specificity of 99.2%. For colorectal cancer, sensitivities for stages I-IV ranged from 76.2 to 83.3% while for lung cancer, sensitivities for stages I-IV ranged from 44.4 to 78.9%, both again at a specificity of 99.2%. The CSO model's true-positive rates were 94.4% and 89.9% for colorectal and lung cancers, respectively. The MRE-Seq was found to be a useful method for detecting global hypomethylation patterns in liquid biopsy samples and accurately diagnosing colorectal and lung cancers, as well as determining CSO of the cancer using DNN analysis.Trial registration: This trial was registered at ClinicalTrials.gov (registration number: NCT04253509) for lung cancer on 5 February 2020, https://clinicaltrials.gov/ct2/show/NCT04253509 . Colorectal cancer samples were retrospectively registered at CRIS (Clinical Research Information Service, registration number: KCT0008037) on 23 December 2022, https://cris.nih.go.kr , https://who.init/ictrp . Healthy control samples were retrospectively registered.

Synthesis of Highly Dispersible Functionalized Carbon Nanotubes as Conductive Material through a Facile Drying Process for High-Power Lithium-ion Batteries.

Herein, surface-functionalized carbon nanotubes (CNTs) were successfully synthesized by dry ball milling that facilitates industrial application. The optimal conditions were determined by analyzing the physicochemical characteristics of CNTs, including the content of the carboxyl group (-COOH) induced on the surface of CNTs by co-existing dry ice based on the ball milling time. Among them, 30 s ball milling (CNTs-30s) showed a high dispersibility in N-methyl-2-pyrrolidone (NMP) while retaining most carboxyl groups and maintaining the intrinsic high conductivity. In the evaluation of rate capability and 5 C/5 C cyclability applied to the Li1+x (Ni1-y-z Coy Mnz )1-x O2 with 60 % Ni (NCM622) cathode, CNTs-30s showed excellent performance based on a well-formed conductive network. Regarding improved dispersion properties and electrochemical performance, the optimal surface functionalization conditions, dispersibility, and electrode properties according to the processing time were analyzed; based on these, the correlation with electrochemical performance was confirmed.

Enhancement of the thermoelectric performance of Cu3SbSe4 particles by controlling morphology using exfoliated selenium nanosheets.

Copper antimony selenide (Cu3SbSe4), a ternary Cu-based material, is a promising p-type thermoelectric material. In this study, the morphology of Cu3SbSe4 particles was controlled using the shape of Se during the synthesis process. To this end, Se particles with a size of 20 µm were exfoliated to form nanosheets through an ultrasonication process without the oxidation of Se. The variation in the morphology of Cu3SbSe4 nanoparticles and nanosheets affected their grain size, thus affecting their electrical conductivity and lattice thermal conductivity owing to the phonon and electron scattering at the interface of the grains. By combining the effects of the morphological variation with the electrical and lattice thermal conductivity, the Cu3SbSe4 synthesized using Se nanosheets exhibited improved thermoelectric performance. The synthesized Cu3SbSe4 nanosheets achieved a maximum thermoelectric figure of merit value of 0.40, which was 1.41 times higher than that of Cu3SbSe4 nanoparticles.

Strongly Coupled Tin(IV) Sulfide-MultiWalled Carbon Nanotube Hybrid Composites and Their Enhanced Thermoelectric Properties.

Herein, tin(IV) sulfide (SnS2) and multiwalled carbon nanotube (MWCNT) composites are fabricated via a simple solution-mixing method in a hydrothermal reactor. SnS2 is closely coupled to the MWCNT surface, thus forming a coaxial nanostructure. Examination by X-ray photoelectron spectroscopy and scanning transmission electron microscopy indicates that the strong interface between SnS2 and the MWCNTs in the composite material is due to the formation of Sn-O and Sn-S bonds. In addition, an examination of the temperature-dependent thermoelectric (TE) properties demonstrates that the SnS2-MWCNT hybrid composite with 3 wt % MWCNTs exhibits the maximum power factor of ∼91.34 µW/(m·K2) at 500 K, which is ∼50 times larger than that of the pristine SnS2. These results highlight the fabrication and enhanced TE properties of hybrid composites via the coupling of SnS2 and MWCNTs.

Facile fabrication of poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate)-coated selenium nanowire/carbon nanotube composite films for flexible thermoelectric applications.

In this study, we synthesized a flexible thermoelectric composite film consisting of poly(3,4-ethylenedioxythiopene)-poly(4-styrenesulfonate)-coated selenium nanowires (PEDOT:PSS-coated Se NWs) and multi-walled carbon nanotubes (MWCNTs) via simple solution mixing. PEDOT:PSS-coated Se NWs were synthesized by coating PEDOT:PSS-on the surface of Se during the process of synthesizing Se NWs. Then, flexible PEDOT:PSS-coated Se/MWCNT composite films were synthesized by filtration. To verify the thermoelectric (TE) potential, the TE properties of the composite film with various MWCNT contents were investigated to determine the optimal conditions for improved TE performance. The maximum power factor of the composite film was ∼73.94 µW (m K2)-1, which is much higher than that of Se and PEDOT:PSS. This study suggests an approach to fabricate flexible inorganic/organic hybrid films with high TE performance.

Enhanced thermoelectric performance of UV-curable silver (I) selenide-based composite for energy harvesting.

Thermoelectric (TE) composites, with photocured resin as the matrix and Ag2Se (AS) as the filler, are synthesized by a digital-light-processing (DLP) based 3D printer. The mixture of diurethane dimethacrylate (DUDMA) and isobornyl acrylate (IBOA) is used as a UV-curable resin because of their low viscosity and high miscibility. Scanning electron microscopy (FE-SEM) images confirm that the filler retains its shape and remains after the UV-curing process. After completing curing, the mechanical and thermoelectric properties of the composite with different AS contents were measured. The addition of the AS filler increases the thermoelectric properties of the cured resin. When the AS contents increase by 30 wt.%, the maximum power factor was obtained (~ 51.5 µW/m·K2 at room temperature). Additionally, due to the phonon scattering effect between the interfaces, the thermal conductivity of composite is lower than that of pristine photoresin. The maximum thermoelectric figure of merit (ZT) is ~ 0.12, which is achieved with 30 wt.% of AS at 300 K with the enhanced power factor and reduced thermal conductivity. This study presents a novel manufacturing method for a thermoelectric composite using 3D printing.