Mechanical and Energy Engineering Works

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2D metal carbides and nitrides (MXenes) for energy storage
(Nature Publishing Group, 2017-01-17) Anasori, Babak; Lukatskaya, Maria R.; Gogotsi, Yury; Mechanical Engineering and Energy, School of Engineering and Technology
The family of 2D transition metal carbides, carbonitrides and nitrides (collectively referred to as MXenes) has expanded rapidly since the discovery of Ti3C2 in 2011. The materials reported so far always have surface terminations, such as hydroxyl, oxygen or fluorine, which impart hydrophilicity to their surfaces. About 20 different MXenes have been synthesized, and the structures and properties of dozens more have been theoretically predicted. The availability of solid solutions, the control of surface terminations and a recent discovery of multi-transition-metal layered MXenes offer the potential for synthesis of many new structures. The versatile chemistry of MXenes allows the tuning of properties for applications including energy storage, electromagnetic interference shielding, reinforcement for composites, water purification, gas- and biosensors, lubrication, and photo-, electro- and chemical catalysis. Attractive electronic, optical, plasmonic and thermoelectric properties have also been shown. In this Review, we present the synthesis, structure and properties of MXenes, as well as their energy storage and related applications, and an outlook for future research.
2D MXenes: Tunable Mechanical and Tribological Properties
(Wiley, 2021-04-28) Wyatt, Brian C.; Rosenkranz, Andreas; Anasori, Babak; Mechanical and Energy Engineering, School of Engineering and Technology
2D transition metal carbides, nitrides, and carbonitrides, known as MXenes, were discovered in 2011 and have grown to prominence in energy storage, catalysis, electromagnetic interference shielding, wireless communications, electronic, sensors, and environmental and biomedical applications. In addition to their high electrical conductivity and electrochemically active behavior, MXenes' mechanical properties, flexibility, and strong adhesion properties play crucial roles in almost all of these growing applications. Although these properties prove to be critical in MXenes' impressive performance, the mechanical and tribological understanding of MXenes, as well as their relation to the synthesis process, is yet to be fully explored. Here, a fundamental overview of MXenes' mechanical and tribological properties is provided and the effects of MXenes' compositions, synthesis, and processing steps on these properties are discussed. Additionally, a critical perspective of the compositional control of MXenes for innovative structural, low-friction, and low-wear performance in current and upcoming applications of MXenes is provided. It is established here that the fundamental understanding of MXenes' mechanical and tribological behavior is essential for their quickly growing applications.
2D Titanium Carbide (MXene) Based Films: Expanding the Frontier of Functional Film Materials
(Wiley, 2021-11) Li, Guohao; Wyatt, Brian C.; Song, Fei; Yu, Changqiang; Wu, Zhenjun; Xie, Xiuqiang; Anasori, Babak; Zhang, Nan; Mechanical and Energy Engineering, School of Engineering and Technology
2D titanium carbide (Ti3C2Tx) MXene films, with their well-defined microstructures and chemical functionality, provide a macroscale use of nano-sized Ti3C2Tx flakes. Ti3C2Tx films have attractive physicochemical properties favorable for device design, such as high electrical conductivity (up to 20 000 S cm–1), impressive volumetric capacitance (1500 F cm–3), strong in-plane mechanical strength (up to 570 MPa), and a high degree of flexibility. Here, the appealing features of Ti3C2Tx-based films enabled by the layer-to-layer arrangement of nanosheets are reviewed. We devote attention to the key strategies for actualizing desirable characteristics in Ti3C2Tx-based functional films, such as high and tunable electrical conductivity, outstanding mechanical properties, enhanced oxidation-resistance and shelf life, hydrophilicity/hydrophobicity, adjustable porosity, and convenient processability. This review further discusses fundamental aspects and advances in the applications of Ti3C2Tx-based films with a focus on illuminating the relationship between the structural features and the resulting performances for target applications. Finally, the challenges and opportunities in terms of future research, development, and applications of Ti3C2Tx-based films are suggested. A comprehensive understanding of these competitive features and challenges shall provide guidelines and inspiration for the further development of Ti3C2Tx-based functional films, and contribute to the advances in MXene technology.
2D transition metal carbides (MXenes) in metal and ceramic matrix composites
(Springer, 2021-06-02) Wyatt, Brian C.; Nemani, Srinivasa Kartik; Anasori, Babak; Mechanical and Energy Engineering, School of Engineering and Technology
Two-dimensional transition metal carbides, nitrides, and carbonitrides (known as MXenes) have evolved as competitive materials and fillers for developing composites and hybrids for applications ranging from catalysis, energy storage, selective ion filtration, electromagnetic wave attenuation, and electronic/piezoelectric behavior. MXenes’ incorporation into metal matrix and ceramic matrix composites is a growing field with significant potential due to their impressive mechanical, electrical, and chemical behavior. With about 50 synthesized MXene compositions, the degree of control over their composition and structure paired with their high-temperature stability is unique in the field of 2D materials. As a result, MXenes offer a new avenue for application driven design of functional and structural composites with tailorable mechanical, electrical, and thermochemical properties. In this article, we review recent developments for use of MXenes in metal and ceramic composites and provide an outlook for future research in this field.
A 3D microfluidic device fabrication method using thermopress bonding with multiple layers of polystyrene film
(IOP, 2015-05) Cao, Yuanzhi; Bontrager-Singer, Jacob; Zhu, Likun; Department of Mechanical Engineering, School of Engineering
In this article, we present a fabrication method that is capable of making (3D) microfluidic devices with multiple layers of homogeneous polystyrene (PS) film. PS film was chosen as the primary device material because of its advantageous features for microfluidics applications. Thermopress is used as a bonding method because it provides sufficient bonding strength while requiring no heterogeneous bonding materials. By aligning and sequentially stacking multiple layers (3 to 20) of patterned PS film that were achieved by a craft cutter, complicated 3D structured microfluidic devices can be fabricated by multiple steps of thermopress bonding. The smallest feature that can be achieved with this method is approximately 100 μm, which is limited by the resolution of the cutter (25 μm) as well as the thickness of the PS films. Bonding characteristics of PS films are provided in this article, including a PS film bonding strength test, bonding precision assessment, and PS surface wettability manipulation. To demonstrate the capability of this method, the design, fabrication, and testing results of a 3D interacting L-shaped passive mixer are presented.
3D Printed ABS and Carbon Fiber Reinforced Polymer Specimens for Engineering Education
(Springer, 2016) Golub, Michael; Guo, Xingye; Jung, Mingyo; Zhang, Jing; Department of Mechanical Engineering, School of Engineering and Technology
Three 3D printed plastic materials, ABS, ABS plus, and CFRP, have been studied for their potential applications in engineering education. Using tensile test, the stress strain curves of the materials have been measured. The Young’s modulus, ultimate strength, and fracture toughness of the materials are calculated from the stress strain curve. The results show that CFRP has the highest stiffness or Young’s modulus. ABS plus has strongest mechanical properties, with highest ultimate strength and fracture toughness. With the measured properties, the 3D printed samples are a viable solution for engineering students to learn mechanical properties of materials.
A Bi-Level Data-Driven Framework for Fault-Detection and Diagnosis of HVAC Systems feature explainability
(Elsevier, 2022-07) Movahed, Paria; Taheri, Saman; Razban, Ali; Mechanical Engineering, School of Engineering and Technology
Machine learning methods have lately received considerable interest for fault detection diagnostic (FDD) analysis of heating, ventilation, and air conditioning (HVAC) systems due to their high detection accuracy. Meanwhile, HVAC malfunctions are regarded as rare occurrences, hence normal operating data samples are much more accessible than data samples in faulty and malfunctioning conditions. The dominating frequency of normal operation in HVAC datasets have also led to heavily biased classification algorithms within the literature. Moreover, the focus of previous literature has been on increasing accuracy of the models while this leads to a high number of false positives (misleading alarms) in the system. To enhance the performance of diagnostic procedures and fill the mentioned gaps, this study proposes a novel data-driven framework. A bi-level machine learning framework is developed for diagnosing faults in air handling units and rooftop units based on principal component analysis (PCA), time series anomaly detection, and random forest (RF). It is shown that PCA can reduce the dataset dimension with one principal component accounting for 95% of data variance. Also, the random forest could classify the faults with 89% precision for single zone AHU, 85% precision for RTU, and 79% for multi-zone AHU.
A Control Oriented Soot Prediction Model for Diesel Engines Using an Integrated Approach
(American Society of Mechanical Engineers, 2021-11-01) Shewale, Mahesh S.; Razban, Ali; Mechanical Engineering, School of Engineering and Technology
Diesel engines have been used in many vehicles and power generation units since a long time due to their less fuel consumption and high trustworthiness. With reference to upcoming emission norms, various engine out emissions have proved to be causing adverse effect on human health and environment. Soot, or particulate matter is one of the major pollutants in diesel engine out emissions and causes various lung related issues. There have been efforts to reduce the amount of soot generated using after-treatment devices like diesel particulate filter (DPF) to filter out particles and get clean tailpipe emissions. These technologies increase load on the system and involves additional maintenance. Also, deposition-based soot sensors have been found to be inoperative in certain scenarios like cold start conditions. In this research work, an effort has been made to develop a phenomenological model that predicts soot mass generated in a Cummins 6.7L diesel engine. The model uses in-cylinder conditions such as pressure, bulk mean temperature, fuel mass flow rate and injector orifice diameter. The difference between soot mass formed and oxidized yields the net amount of soot generated at engine out end. Furthermore, the generated soot mass is compared with benchmark results for specific load conditions and appropriate controller is designed to minimize this tradeoff. The control parameter being used here is fuel rail pressure, which controls the lift-off length, and ultimately equivalence ratio, which predicts mass of soot, generated in formation phase. The presented method shows a prediction error ranging from 5–20%, which is significantly reduced to 2% using a PID controller. The approach presented in this research work is generic and can be operated as stand-alone system or an integrated subsystem in a higher order control architecture.
A cooperative degradation pathway for organic phenoxazine catholytes in aqueous redox flow batteries
(Elsevier, 2023-03) Fang, Xiaoting; Zeng, Lifan; Li, Zhiguang; Robertson, Lily A.; Shkrob, Ilya A.; Zhang , Lu; Wei, Xioaliang; Mechanical Engineering, School of Engineering and Technology
Redox-active organic molecules that store positive charge in aqueous redox flow cells (catholyte redoxmers) frequently exhibit poor chemical stability for reasons that are not entirely understood. While for some catholyte molecules, deprotonation in their charged state is resposible for shortening the lifetime, for well designed molecules that avoid this common fate, it is seldom known what causes their eventual decomposition as it appears to be energetically prohibitive. Here, a highly soluble (1.6 M) phenoxazine molecule with a redox potential of 0.48 V vs. Ag/AgCl has been examined in flow cells. While this molecule has highly reversible redox chemistry, during cycling the capacity fades in a matter of hours. Our analyses suggest a cooperative decomposition pathway involving disproportionation of two charged molecules followed by anion substitution and deprotonation. This example suggests that cooperative reactions can be responsible for unexpectedly low chemical instability in the catholyte redoxmers and that researchers need to be keenly aware of such reactions and methods for their mitigation.
A Novel Framework for Predictive Modeling and Optimization of Powder Bed Fusion Process
(MDPI, 2021-10) Marrey, Mallikharjun; Malekipour, Ehsan; El-Mounayri, Hazim; Faierson, Eric J.; Agarwal, Mangilal; Mechanical and Energy Engineering, School of Engineering and Technology
Powder bed fusion (PBF) process is a metal additive manufacturing process which can build parts with any complexity from a wide range of metallic materials. PBF process research has predominantly focused on the impact of only a few parameters on product properties due to the lack of a systematic approach for optimizing a large set of process parameters simultaneously. The pivotal challenges regarding this process require a quantitative approach for mapping the material properties and process parameters onto the ultimate quality; this will then enable the optimization of those parameters. In this study, we propose a two-phase framework for optimizing the process parameters and developing a predictive model for 316L stainless steel material. We also discuss the correlation between process parameters -- i.e., laser specifications -- and mechanical properties and how to achieve parts with high density (> 98%) as well as better ultimate mechanical properties. In this paper, we introduce and test an innovative approach for developing AM predictive models, with a relatively low error percentage of 10.236% that are used to optimize process parameters in accordance with user or manufacturer requirements. These models use support vector regression, random forest regression, and neural network techniques. It is shown that the intelligent selection of process parameters using these models can achieve an optimized density of up to 99.31% with uniform microstructure, which improves hardness, impact strength, and other mechanical properties.

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