Problems with latent heat thermal storage (LHTS) often contain several boundary conditions that an exact solution cannot solve. Therefore, novel methods to tackle such issues could fundamentally change the design of innovative energy storage systems. This study concentrates on the reformulation of the generalized differential quadrature method (GDQM) for the two-region freezing/melting Stefan problem as an essential LHTS challenge. Comparison and convergence show that there is sufficient confidence in the proposed approach. By monitoring the precision of the suggested approach for the LHTS problem, it was indicated that this method's error depends on Stefan's number. The maximum error of all Stefan numbers up to 0.3 is less than 6%. For such applications in a standard array of LHTS (Stefan numbers between 0 and 0.2), the proposed method is appropriate as it predicts the answers with a maximum of 4.2% error. In comparison to the heat capacity method, GDQM delivers a more precise result at higher processing times. Additionally, this GDQM priority is accompanied by a low computational cost, which is unquestionably superior.
V-shaped and triangular cantilevers are widely employed in atomic force microscope (AFM) imaging techniques due to their stability. For the design of vibration control systems of AFM cantilevers which utilize patched piezo actuators, obtaining an accurate system model is indispensable prior to acquiring the information related to natural modes. A general differential quadrature element method (GDQEM) analysis based on layer-wise displacement beam theory was performed to obtain the natural frequencies of V-shaped AFM cantilevers with piezoelectric actuators. A finite element analysis was applied to validate the accuracy of numerical results. Finally, a parametric investigation of the sensitivity of natural frequencies with respect to beam geometry was performed. Simulations show that presented approach is considerably accurate and does not need a lot computational costs. Based on the governing equations, general differential quadrature method (GDQM) and GDQM could be applied for uniform and stepped plates, respectively. Thus, presented approach covers the V-shaped and triangular cantilevers perfectly and could be utilized to derive the
Abstract Carbon, nitrogen, and boron nanostructures are promising ballistic protection materials due to their low density and excellent mechanical properties. In this study, the ballistic properties of C3N and BC3 nanosheets against hypersonic bullets with Mach numbers greater than 6 were studied. The critical perforation conditions, and thus, the intrinsic impact strength of these 2D materials were determined by simulating ballistic curves of C3N and BC3 monolayers. Furthermore, the energy absorption scaling law with different numbers of layers and interlayer spacing was investigated, for homogeneous or hybrid configurations (alternated stacking of C3N and the BC3). Besides, we created a hybrid sheet using van der Waals bonds between two adjacent sheets based on the hypervelocity impacts of fullerene (C60) molecules utilizing molecular dynamics simulation. As a result, since the higher bond energy between N–C compared to B-C, it was shown that C3N nanosheets have higher absorption energy than BC3. In contrast, in lower impact speeds and before penetration, single-layer sheets exhibited almost similar behavior. Our findings also reveal that in hybrid structures, the C3N layers will improve the ballistic properties of BC3. The energy absorption values with a variable number of layers and variable interlayer distance (X = 3.4 Å and 4X = 13.6 Å) are investigated, for homogeneous or hybrid configurations. These results provide a fundamental understanding of ultra-light multilayered armors' design using nanocomposites based on advanced 2D materials. The results can also be used to select and make 2D membranes and allotropes for DNA sequencing and filtration.
Abstract Diabetic ketoacidosis (DKA), a severe complication of type 1 diabetes (T1D), is triggered by production of large quantities of ketone bodies, requiring patients with T1D to constantly monitor their ketone levels. Here, a skin‐compatible hydrogel microneedle (HMN)‐continuous ketone monitoring (HMN‐CKM) device is reported. The sensing mechanism relies on the catechol–quinone chemistry inherent to the dopamine (DA) molecules that are covalently linked to the polymer structure of the HMN patch. The DA serves the dual‐purpose of acting as a redox mediator for measuring the byproduct of oxidation of 3‐beta‐hydroxybutyrate (β‐HB), the primary ketone bodies; while, also facilitating the formation of a crosslinked HMN patch. A universal approach involving pre‐oxidation and detection of the generated catechol compounds is introduced to correlate the sensor response to the β‐HB concentrations. It is further shown that real‐time tracking of a decrease in ketone levels of T1D rat model is possible using the HMN‐CKM device, in conjunction with a data‐driven machine learning model that considers potential time delays.
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