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Crystals, Volume 14, Issue 12 (December 2024) – 5 articles

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19 pages, 2599 KiB

Open AccessArticle

Microstructural and Mechanical Properties of Dissimilar AA7075 and AA2024 Rotary Friction Weldments

by Sandip Kumar Bauri, Nagumothu Kishore Babu, Malkapuram Ramakrishna, Ateekh Ur Rehman, Vanam Jaya Prasad and Minnam Reddy Suryanarayana Reddy

Crystals 2024, 14(12), 1011; https://doi.org/10.3390/cryst14121011 - 21 Nov 2024

Abstract

This study aims to explore the effects of various pre- and post-weld heat treatments (PWHTs) on the microstructural and mechanical properties of dissimilar aluminium alloys, namely AA7075 and AA2024, joined through rotary friction welding. The joints were rigorously evaluated through multiple characterization methods, revealing no signs of cracking or incomplete bonding. This study observed that dissimilar joints between AA7075 and AA2024 alloys showed increased flash formation on the AA7075 side due to its lower melting point relative to the AA2024 alloy. Various zones within the weld region were identified, such as the dynamic recrystallized zone (DRZ), the thermo-mechanically affected zone (TMAZ)—which includes TMAZ-1 with elongated grains and TMAZ-2 with compressed or distorted grains—the heat-affected zone (HAZ), and the base metal (BM) zone. Of all the welding conditions examined, the post-weld heat-treated (PWHT) AA2024/AA7075 joint produced by rotary friction welding showed the highest strength, with a yield strength (YS) of 305 ± 2 MPa and an ultimate tensile strength (UTS) of 477 ± 3 MPa. This improvement in strength can be attributed to the significant strengthening precipitates of MgZn₂ (found on the AA7075 side), θ-Al₂Cu, and S-Al₂CuMg (found on the AA2204 side) formed during post-weld ageing. Notably, all dissimilar welds failed in the HAZ region on the AA2024 side due to coarse grain formation, identifying this as the weakest area. Full article

(This article belongs to the Section Crystalline Metals and Alloys)

16 pages, 7049 KiB

Open AccessArticle

Revealing the Relationship Between Macrostructures and Inclusions Across the Thickness Direction of Q235B Slabs

by Bo Wang, Jinwen Jin, Chao Gu, Ze Wei, Ziyu Lyu, Lidong Xing and Yanping Bao

Crystals 2024, 14(12), 1010; https://doi.org/10.3390/cryst14121010 - 21 Nov 2024

Abstract

Macrostructures and inclusions are both vital for slabs because the quality of slabs is largely affected by them. However, the relationship between macrostructures and inclusions in the thickness direction of the slab is still unclear. Hence, in this paper, the relationship between macrostructures and inclusions was revealed by laboratory experiments and theoretical calculations. The laboratory experiments included carbon and sulfur content testing, direct reading spectroscopy, scanning electron microscopy, and automatic inclusion scanning. The experimental results showed that the distribution of macrostructures was symmetrical from the inner and outer arc to the center. From the edge to the center of the slab, the variation in macrostructures was columnar crystal zone (CZ)→columnar-to-equiaxed transition (CET)→equiaxed crystal zone (EZ). Furthermore, the content of sulfur and manganese first decreased and then increased from the inner arc to the outer arc. The number density and area fraction of MnS inclusions in different macrostructures were CZ > CET > EZ. The average size of MnS in different macrostructures was CZ > EZ > CET. Moreover, the morphology of MnS inclusions was ellipse and rod in CZ, irregular dendrite in CET, and multilateral in EZ. Additionally, theoretical calculation results showed the maximum precipitation and initial precipitation temperature of MnS inclusions in different macrostructures were CZ > EZ > CET. Meanwhile, the theoretical precipitation radius of MnS inclusions in different macrostructures was CZ > EZ > CET. Full article

(This article belongs to the Section Inorganic Crystalline Materials)

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16 pages, 4032 KiB

Open AccessArticle

In Situ Microscopy with Real-Time Image Analysis Enables Online Monitoring of Technical Protein Crystallization Kinetics in Stirred Crystallizers

by Julian Mentges, Daniel Bischoff, Brigitte Walla and Dirk Weuster-Botz

Crystals 2024, 14(12), 1009; https://doi.org/10.3390/cryst14121009 - 21 Nov 2024

Abstract

Controlling protein crystallization processes is essential for improving downstream processing in biotechnology. This study investigates the combination of machine learning-based image analysis and in situ microscopy for real-time monitoring of protein crystallization kinetics. The experimental research is focused on the batch crystallization of an alcohol dehydrogenase from Lactobacillus brevis (LbADH) and two selected rational crystal contact mutants. Technical protein crystallization experiments were performed in a 1 L stirred crystallizer by adding polyethyleneglycol 550 monomethyl ether (PEG 550 MME). The estimated crystal volumes from online microscopy correlated well with the offline measured protein concentrations in solution. In addition, in situ microscopy was superior to offline data if amorphous protein precipitation occurred. Real-time image analysis provides the data basis for online estimation of important batch crystallization performance indicators like yield, crystallization kinetics, crystal size distributions, and number of protein crystals. Surprisingly, one of the LbADH mutants, which should theoretically crystallize more slowly than the wild type based on molecular dynamics (MD) simulations, showed better crystallization performance except for the yield. Thus, online monitoring of scalable protein crystallization processes with in situ microscopy and real-time image analysis improves the precision of crystallization studies for industrial settings by providing comprehensive data, reducing the limitations of traditional analytical techniques, and enabling new insights into protein crystallization process dynamics. Full article

(This article belongs to the Section Biomolecular Crystals)

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(a) Schematic drawing of the stirred 1 L crystallizer, with integrated in situ microscopy probe, agitator, and sampling tube, with a schematic enlargement of the probe cleft and the expected particle flow profile. (b) Photograph of the stirred 1 L crystallizer with double jacket for temperature control and the in situ microscopy probe. Full article ">Figure 2
(a) Crystallization experiments of the LbADH WT in a stirred 1 L crystallizer to identify the ratio of maximum local energy dissipation to mean power input by varying the stirrer speed from 50 rpm (yellow), 75 rpm (gray) and 100 rpm (blue) (c0 = 5 g L−1, 100 g L−1 PEG MME 550, 100 mM Tris-HCl, 50 mM MgCl2, pH 7.0, T = 20 °C). The black dotted vertical line indicates the start of the crystallization (3 h), determined as the point at which a decrease of more than 1% in protein concentration was observed between two consecutive measurements; (b) Furthermore, the respective yields (gray) and the maximum crystallization speeds (yellow), obtained from the logistic fits, are plotted against the stirrer speed. The error bars (min-max values) result from carrying out the experiments twice. Full article ">Figure 3
(a) Illustration of LbADH WT crystallization experiments in a stirred 1 L crystallizer. The online observed crystal volume (light gray, dark gray) and the offline measured protein concentration (blue) in the supernatant, as well as the average crystallization volume (orange), are depicted. (b) Furthermore, the non-soluble protein concentration (blue) and the respective logistic fits (blue, orange) are shown. The sampling rate of the automatic image evaluation was 0.016 Hz. A moving average considering the surrounding 10 values was used to smooth the raw data of the crystal volume. (c) An exemplary photomicrograph after 8 h crystallization is shown (left). This photomicrograph was also evaluated by the image analysis software, and the crystals detected were marked (center). The crystal agglomerate formation at the end of the process can be seen in the (right) photomicrograph. (c0 = 5 g L−1, 100 g L−1 PEG 550 MME, ns = 50 rpm, 100 mM Tris-HCl, 50 mM MgCl2, pH 7.0, T = 20 °C). Full article ">Figure 4
(a) Online observed crystal volume (orange) and offline measured non-soluble protein concentration (blue) with the corresponding logistic fits during the batch crystallization experiment of the LbADH WT with initial amorphous precipitation due to rapid addition of the crystallization buffer. A moving average considering the surrounding 10 values was used to smooth the raw data of the crystal volume. (b) In addition, two representative photomicrographs at the beginning (left) and at the end of the crystallization process (right) are shown. (fs = 0.16 Hz, c0 = 5 g L−1, 100 g L−1 PEG 550 MME, ns = 50 rpm, 100 mM Tris-HCl, 50 mM MgCl2, pH 7.0, T = 20 °C). Full article ">Figure 5
Online observed crystal volume (light gray, dark gray) and offline measured protein in the supernatant or the non-soluble protein concentration during batch crystallization experiments of the LbADH mutants Q207D (green) and T102E (red) in a stirred 1 L crystallizer, as well as the average crystallization volume (orange). Furthermore, the respective logistic fits (green, red, orange) are shown. In addition, a moving average considering the surrounding 10 values was used to smooth the raw data of the crystal volume (fs = 0.16 Hz, c0 = 5 g L−1, 100 g L−1 PEG 550 MME, ns = 50 rpm, 100 mM Tris-HCl, 50 mM MgCl2, pH 7.0, T = 20 °C). Full article ">Figure 6
Final size distributions of protein crystals of the LbADH WT and the mutants T102E and Q207D (bars: crystal count; line: interpolated cumulative distributions). Shown are the distributions (intervals of 2.5 µm) of the length (left) and width (right) of protein crystals after 24 h stirred crystallization on a 1 L scale (c0 = 5 g L−1, 100 g L−1 PEG 550 MME, ns = 50 rpm, 100 mM Tris-HCl, 50 mM MgCl2, pH 7.0, T = 20 °C). Full article ">

13 pages, 5425 KiB

Open AccessArticle

Highly Sensitive SnS₂/rGO-Based Gas Sensor for Detecting Chemical Warfare Agents at Room Temperature: A Theoretical Study Based on First-Principles Calculations

by Ting Liang, Huaizhang Wang, Huaning Jiang, Yelin Qi, Rui Yan, Jiangcun Li and Yanlei Shangguan

Crystals 2024, 14(12), 1008; https://doi.org/10.3390/cryst14121008 - 21 Nov 2024

Abstract

Chemical warfare agents (CWAs) are known as poor man’s bombs because of their small lethal dose, cheapness, and ease of production. Therefore, the highly sensitive and rapid detection of CWAs at room temperature (RT = 25 °C) is essential. In this paper, we have developed a resistive semiconductor sensor for the highly sensitive detection of CWAs at RT. The gas-sensing material is SnS₂/rGO nanosheets (NSs) prepared by hydrothermal synthesis. The lower detection limits of the SnS₂/rGO NSs-based gas sensor were 0.05 mg/m³ and 0.1 mg/m³ for the typical chemical weapons sarin (GB) and sulfur mustard (HD), respectively. The responsivity can reach −3.54% and −10.2% in 95 s for 1.0 mg/m³ GB, and in 47 s for 1.0 mg/m³ HD. They are 1.17 and 2.71 times higher than the previously reported Nb-MoS₂ NSs-based gas sensors, respectively. In addition, it has better repeatability (RSD = 6.77%) and stability for up to 10 weeks (RSD = 20.99%). Furthermore, to simplify the work of later researchers based on the detection of CWAs by two-dimensional transition metal sulfur compounds (2D-TMDCs), we carried out calculations of the SnS₂ NSs-based and SnS₂/rGO NSs-based gas sensor-adsorbing CWAs. Detailed comparisons are made in conjunction with experimental results. For different materials, it was found that the SnS₂/rGO NSs-based gas sensor performed better in all aspects of adsorbing CWAs in the experimental results. Adsorbed CWAs at a distance smaller than that of the SnS₂ NSs-based gas sensor in the theoretical calculations, as well as its adsorption energy and transferred charge, were larger than those of the SnS₂ NSs-based gas sensor. For different CWAs, the experimental results show that the sensitivity of the SnS₂/rGO NSs-based gas sensor for the adsorption of GB is higher than that of HD, and accordingly, the theoretical calculations show that the adsorption distance of the SnS₂/rGO NSs-based gas sensor for the adsorption of GB is smaller than that of HD, and the adsorption energy and the amount of transferred charge are larger than that of HD. This regularity conclusion proves the feasibility of adsorption of CWAs by gas sensors based on SnS₂ NSs, as well as the feasibility and reliability of theoretical prediction experiments. This work lays a good theoretical foundation for subsequent rapid screenings of gas sensors with gas-sensitive materials for detecting CWAs. Full article

(This article belongs to the Special Issue Organic Photonics: Organic Optical Functional Materials and Devices)

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(a) Sensor electrode physical picture; (b) SEM images of the SnS2/rGO NSs; (c) TEM images of the SnS2/rGO NSs; (d) the high-resolution TEM image. Full article ">Figure 2
SnS2/rGO (a) XRD characterization; (b) Raman spectra; (c) EDS elemental mapping. Full article ">Figure 3
The response–recovery curve of the SnS2 and SnS2/rGO NSs-based gas sensor was exposed to various concentrations of (a) GB and (b) HD vapor ranging from 0.05 to 1.5 mg/m3. (c) Three successive sensing cycles of the SnS2 NSs-based and SnS2/rGO NSs-based gas sensors were continuously exposed to 0.1 mg/m3 GB. (d) Long-term stability of the SnS2/rGO NSs-based gas sensor was exposed to 0.5 mg/m3 GB for ten weeks. Full article ">Figure 4
Sensing schematic diagram of SnS2/rGO NSs (a) in air and (b) adsorption GB. Full article ">Figure 5
Structural modeling of (a) SnS2; (b) SnS2/rGO; (c) GB; and (d) HD. Optimal adsorption sites of GB on (e) SnS2 and (f) SnS2/rGO surfaces. Optimal adsorption sites of HD on (g) SnS2 and (h) SnS2/rGO surfaces. Full article ">Figure 6
Differential charge-density plots of SnS2 adsorption on (a) GB and (b) HD; differential charge-density plots of SnS2/GO adsorption on (c) GB and (d) HD. (The isosurfaces take the value of 0.02 eV/Å. Green is the region of concentration of electrons. Light blue is the region of dissipation of electrons). Full article ">Figure 7
(a) Energy band structure and (b) density-of-state plots for SnS2. (c) Energy band structure and (d) density-of-state plots for SnS2/rGO. (e) Energy band structure and (f) density-of-state plots of SnS2/rGO NSs-adsorbed GB. (g) Energy band structure and (h) density-of-state plots of SnS2/rGO NSs-adsorbed HD. Full article ">

22 pages, 6414 KiB

Open AccessArticle

Experimental Investigation and Machine Learning Modeling of Tribological Characteristics of AZ31/B₄C/GNPs Hybrid Composites

by Dhanunjay Kumar Ammisetti, Bharat Kumar Chigilipalli, Baburao Gaddala, Ravi Kumar Kottala, Radhamanohar Aepuru, T. Srinivasa Rao, Seepana Praveenkumar and Ravinder Kumar

Crystals 2024, 14(12), 1007; https://doi.org/10.3390/cryst14121007 - 21 Nov 2024

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Abstract

In this study, the AZ31 hybrid composites reinforced with boron carbide (B₄C) and graphene nano-platelets (GNPs) are prepared by the stir casting method. The main aim of the study is to study the effect of various wear parameters (reinforcement percentage (R), applied load (L), sliding distance (D), and velocity (V)) on the wear characteristics (wear rate (WR)) of the AZ91/B₄C/GNP composites. Experiments are designed using the Taguchi technique, and it was determined that load (L) is the most significant parameter affecting WR, followed by D, R, and V. The wear mechanisms under conditions of maximum and minimum wear rates are examined using SEM analysis of the worn-out surfaces of the specimens. From the result analysis on the WR, the ideal conditions for achieving the lowest WR are R = 4 wt.%, L = 15 N, V = 3 m/s, and D = 500 m. Machine learning (ML) models, including linear regression (LR), polynomial regression (PR), random forest (RF), and Gaussian process regression (GPR), are implemented to develop a reliable prediction model that forecasts output responses in accordance with input variables. A total of 90% of the experimental data points were used to train and 10% to evaluate the models. The PR model exceeded the accuracy of other models in predicting WR, with R² = 0.953, MSE = 0.011, RMSE = 0.103, and COF with R² = 0.937, MSE = 0.013, and RMSE = 0.114, respectively. Full article

(This article belongs to the Special Issue Feature Papers on "Hybrid and Composite Crystalline Materials" 2023–2024)

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