•This article reviews the state-of-the-art of existing DSC literature in detail from both academic and industrial points of view.•It identifies key limitations and prospects in DSC, summarizes prior ...research and identifies knowledge gaps.•It provides a development framework as a roadmap for future research and practice.
Suppliers, partners, companies and dealers in supply chains do use, generate and share information with others. These associations lead to a multitude of challenges and opportunities within the supply chains. A Digital Supply Chain (DSC) is a smart, value-driven, efficient process to generate new forms of revenue and business value for organizations and to leverage new approaches with novel technological and analytical methods DSC is not about whether goods and services are digital or physical, it is about the way how supply chain processes are managed with a wide variety of innovative technologies, e.g. unmanned aerial vehicles, cloud computing, and internet of things, among others. Recent literature highlights the importance of DSC and many industrial researchers discuss its applications. This article reviews the state-of-the-art of existing DSC literature in detail from both academic and industrial points of view. It identifies key limitations and prospects in DSC, summarizes prior research and identifies knowledge gaps by providing advantages, weaknesses and limitations of individual methods The article also aims at providing a development framework as a roadmap for future research and practice.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
This paper reviews the best-known differential scanning calorimetries (DSCs), such as conventional DSC, microelectromechanical systems-DSC, infrared-heated DSC, modulated-temperature DSC, gas ...flow-modulated DSC, parallel-nano DSC, pressure perturbation calorimetry, self-reference DSC, and high-performance DSC. Also, we describe here the most extensive applications of DSC in biology and nanoscience.
In this article, the adaptive finite‐time quantized and event‐triggered dynamic surface control (DSC) schemes are proposed for a class of strict‐feedback nonlinear system with time varying input ...delay. In order to ensure the completeness of the DSC method, an improved dynamic surface control method based on command filtering is adopted. The designed filtering error compensation signal can not only effectively compensate the filtering error, but also deal with the discontinuous input possessing unknown input delay. The problem of finite‐time control design and stability analysis with the difficulties brought by discontinuous input with unknown time‐varying input delay is solved through the design of the new compensation signal and the corresponding Lyapunov functional. Theoretical analysis shows that all signals in the closed‐loop system are bounded in finite time. The simulation examples also verify the effectiveness of the adaptive finite time DSC schemes proposed in this article.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
•Fast DSC with liquids in the temperature range −100°C to +225°C, at scan rates up to 1000°C/s.•Reproducibility of calculated enthalpy values ca ±7%, of peak temperature values ca ±1°C.•Resolution ...for measuring lysozyme denaturation between 0.1% and 1% lysozyme solution in water by weight.•Measurements for bovine serum show good agreement with DSC results, but typically 1000× faster.•Results for olive oil give good agreement for the freezing peak, a temperature shift for the melting peak compared to DSC.
Based on a modified version of standard chips for fast differential scanning calorimetry, DSC of liquid samples has been performed at temperature scan rates of up to 1000°C/s. This paper describes experimental results with the protein lysozyme, bovine serum, and olive oil. The heating and cooling rate of the sensor is measured for temperature scan rates of up to 1300°C/s with water and 2-butanol, in the temperature range of −90°C/s to +130°C/s. The lysozyme is measured at temperature scan rates varying from 10°C/s to 400°C/s and in concentrations between 0.1% and 10% protein by weight. The bovine serum measurements show two main peaks, in good agreement with standard DSC measurements. Olive oil has been measured, with good agreement for the cooling curve and qualitative agreement for the heater curve, compared to DSC measurements.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Multi-echo, multi-contrast methods are increasingly used in dynamic imaging studies to simultaneously quantify R2∗ and R2. To overcome the computational challenges associated with nonlinear least ...squares (NLSQ) fitting, we propose a generalized linear least squares (LLSQ) solution to rapidly fit R2∗ and R2.
Spin- and gradient-echo (SAGE) data were simulated across T2∗ and T2 values at high (200) and low (20) SNR. Full (four-parameter) and reduced (three-parameter) parameter fits were implemented and compared with both LLSQ and NLSQ fitting. Fit data were compared to ground truth using concordance correlation coefficient (CCC) and coefficient of variation (CV). In vivo SAGE perfusion data were acquired in 20 subjects with relapsing-remitting multiple sclerosis. LLSQ R2∗ and R2, as well as cerebral blood volume (CBV), were compared with the standard NLSQ approach.
Across all fitting methods, T2∗ was well-fit at high (CCC = 1, CV = 0) and low (CCC ≥ 0.87, CV ≤ 0.08) SNR. Except for short T2∗ values (5–15 ms), T2 was well-fit at high (CCC = 1, CV = 0) and low (CCC ≥ 0.99, CV ≤ 0.03) SNR. In vivo, LLSQ R2∗ and R2 estimates were similar to NLSQ, and there were no differences in R2∗ across fitting methods at high SNR. However, there were some differences at low SNR and for R2 at high and low SNR. In vivo NLSQ and LLSQ three parameter fits performed similarly, as did NLSQ and LLSQ four-parameter fits. LLSQ CBV nearly matched the standard NLSQ method for R2∗- (0.97 ratio) and R2-CBV (0.98 ratio). Voxel-wise whole-brain fitting was faster for LLSQ (3–4 min) than NLSQ (16–18 h).
LLSQ reliably fit for R2∗ and R2 in simulated and in vivo data. Use of LLSQ methods reduced the computational demand, enabling rapid estimation of R2∗ and R2.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This study explores the application of an Internet of Things (IoT)-driven reflectance-based multimode colorimeter for real-time monitoring of the crystallization process in oleogels-a novel class of ...structured lipids gaining popularity in the food industries. These structured lipids offer a healthier alternative to solid fats, but their texture and stability rely on precise control of crystallization process. Traditional monitoring methods, such as atomic force microscopy and spectroscopy, are expensive and lack real-time capabilities. The proposed device can operate in two modes: quality testing and process monitoring modes. In the quality testing mode, the device exhibits superior color accuracy compared to a commercial device, making it a reliable tool for color assessment (ΔE values < 10). In the process monitoring mode, the device effectively tracks crystallization kinetics at different incubation temperatures (5 °C, 15 °C, and 25 °C), revealing the impact of sunflower lecithin on primary and secondary crystallization phases. Further, the temperature vs. L* data offers more profound insights into oleogel crystallization, validated by Differential Scanning Calorimetry (DSC) analysis. Additionally, the device's performance was tested by monitoring the crystallization process of butter. The results obtained from the device closely matched the DSC findings, which enhanced our understanding of the crystallization processes in butter. This showcases the potential of the device for analyzing food samples.
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•Development of an innovative IoT-driven reflectance colorimeter working in quality testing and process monitoring modes.•Quality Testing mode demonstrated better color accuracy compared to a commercial device.•In Process Monitoring mode, the device effectively tracked the crystallization kinetics of oleogels.•Strong positive correlations between the proposed device and DSC results validate its efficiency.•The testing of the developed device using butter sample is matched with the result obtained from DSC analysis.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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•Oxidation of carbon nanomaterial was performed both in TGA and ETEM.•The onset of oxidation upon heating was similar between TGA and ETEM.•ETEM enable nanoscale visualization of the ...dynamic oxidation process in real time.•Etching and layer-by-layer oxidation occurs for few layers graphene and MWCNT.•Nanodiamonds and C60 affects by structural changes and ebeam effects upon heating.
The development of a model of carbon oxidation has engaged researchers for decades. Yet many outstanding questions remain due to the inability to experimentally study the details of the oxidation. Today, novel techniques such as environmental transmission electron microscopy (ETEM), allowing for in-situ nanoscale observations of the oxidation process, can help illuminate some of these questions. In this study of few layer graphene (FLG), multi-walled carbon nanotubes (MWCNTs), buckminsterfullerene (C60), and nanodiamonds (NDs) oxidizing in temperatures up to 1100 °C and we analyze the importance of nanostructure for the thermal stability of nanocarbons. The study was complemented with thermogravimetric analysis (TGA) and the experiments were in good agreement with oxidation rates increasing sharply with temperature and the thermal stability of the materials MWCNTs, FLG, C60 and NDs in descending order. Based on the direct nanoscale visualization obtained in the ETEM the materials can be divided into two overall categories: materials with low strain sp2-bonds (FLG and MWCNT); and materials with high strain sp2-bonds (C60) or sp3-bonds (NDs). For materials in the first category, it is possible to identify several different phenomena as their oxidation rate increases as a function of temperatures whereas materials in the second category appear to be more influenced by extrinsic factors such as the electron beam and by structural transformation upon heating. This study clearly shows the value of adding ETEM results to traditional TGA investigations since it gives both a complementary and more detailed information about the dynamic oxidation process.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The Rock-Eval pyrolysis-stage derived parameters such as free hydrocarbons (S1), heavier pyrolysis-hydrocarbons (S2), pyrolyzable carbon (PC) and pyrolysis Tmax (from S2 curve) have received ...considerable interest for source-rock screening and thermal maturity assessment. On the other hand, the Rock-Eval oxidation-stage S4CO2 curve, which gives the amount of residual carbon (RC), only recently has received some interest. While the pyrolysis-stage S2 temperature-peak (Tmax) is conventionally used as a maturity proxy, in this work we show that the temperature-peak of S4CO2 curve (S4Tmax) can also be used as a thermal maturity proxy for shales. For overmature and low-TOC shale samples, showing asymmetric S2 shape and concomitantly producing doubtful Tmax, the S4 curves showed symmetric nature and consequently the S4Tmax was observed to be a reliable thermal maturity estimate. While the S4Tmax clearly resolved immature and overmature shales, for the early mature and peak mature shales the S4Tmax showed overlapping values. S4Tmax of pre-pyrolyzed and pyrolyzed masses showed good positive correlation with differential scanning calorimetry temperature-peak (DSCTpeak), and consequently indicated its applicability as a thermal maturity proxy. When early mature pre-pyrolyzed samples were directly analyzed using the Rock-Eval oxidation stage, the S4 curves showed formation of two sub-peaks, and consequently the Tmax was observed to decrease. It is recommended that analysts and interpreters should thoroughly cross-check S2 curves before reporting data, and in case of asymmetric or unreliable S2 curves, the S4Tmax can be used as a maturity proxy.
•Importance of Rock-Eval oxidation stage.•S4Tpeak as a thermal maturity proxy for shales.•Critical monitoring of Rock-Eval S2 curves.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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