Olabode Ajenifujah

Carnegie Mellon University

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Levi T. Thompson

University of Michigan–Ann Arbor

Amir Barati Farimani

Carnegie Mellon University

Francis Ogoke

Carnegie Mellon University

Abdoulaye Djire

Texas A&M University

Alice Sleightholme

University of Michigan–Ann Arbor

Paul G. Rasmussen

California Institute of Technology

Jack Beuth

Carnegie Mellon University

Jason B. Siegel

University of Michigan–Ann Arbor

Lilin He

Oak Ridge National Laboratory

Achuth Chandrasekhar

Cooperative Institutions

University of Michigan–Ann Arbor

Carnegie Mellon University

University of Tehran

Lawrence Berkeley National Laboratory

Ann Arbor Center for Independent Living

Michigan United

Massachusetts Institute of Technology

Imperial College London

Zhejiang University

University of California, Berkeley

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Charge-Storage Mechanisms in Nanostructured Carbides and Nitrides for Energy Storage

Meeting abstracts/Meeting abstracts (Electrochemical Society. CD-ROM) (2015)

Abdoulaye Djire Olabode Ajenifujah Alice Sleightholme Paul G. Rasmussen Lilin He

Introduction Early transition-metal carbides and nitrides are potential electrode materials for supercapacitor applications due to their high accessible surface areas, high electric conductivities and low cost. They possess high capacitances, good capacitance retention during cycling and wide voltage windows. For example, the capacitance for VN has been reported to be as high as 1340 Fg -1 in aqueous electrolyte [1]. The origin of this high capacitance was attributed to a combination of electric double-layer formation and faradaic redox reactions occurring on the nitride or oxynitride (VNxOy) surface. However, the contribution of the each individual mechanism is ill-defined. Despite efforts to date, the nature of the pseudocapacitive properties of early transition-metal carbides and nitrides remains vague. Full exploitation of the properties of these materials will require an understanding of the pseudocapacitive charge-storage mechanism. Here we report a detailed investigation of the charge-storage mechanisms for early transition-metal carbides and nitrides in aqueous media. The pseudocapacitive charge-storage mechanism has been investigated using x-ray absorption spectroscopy and neutron scattering and a combination of electrochemical techniques including cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS). Experimental High-surface-area Ti, V, Mo, and W carbides and nitrides were prepared from their oxide precursors TiO 2 (Alfa Aesar), V 2 O 5 (Alfa Aesar), (NH 4 ) 6 Mo 7 O 24 .4H 2 O (81-83% as MoO 3 , Alfa Aesar) and WO 3 (Alfa Aesar) respectively, by temperature-programmed reactions (TPR) [2]. Characterization of the structural properties was performed using nitrogen physisorption (BET surface area) and X-ray diffraction. The total capacitance and extent of pseudocapacitance was determined based on results from CV and EIS. For selected materials details regarding the adsorption of actives species and metal oxidation state changes during electrochemical cycling were determined using neutron scattering and x-ray absorption. Results and Discussion Table 1 lists the total capacitances and percent contribution of pseudocapacitance of each material in aqueous electrolytes. The pseudocapacitive charge storage was obtained by subtracting the double-layer capacitance from the total capacitance. The extent of pseudocapacitive charge storage contribution ranged from 61% for TiN to 88% for WC 1-x in acid and base electrolytes, respectively. This result indicates that pseudocapacitance is the dominant charge-storage mechanism in carbides and nitrides. This is expected given that these materials are electroactive and can go through several oxidation state changes during electrochemical cycling. Figure 1 shows the oxidation state changes for Mo 2 N in acidic electrolyte during electrochemical cycling. There was removal of one electron per Mo as the potential was increased. This result suggests reduction of the metal during electrochemical cycling. Concomitant with changes in oxidation state, neutron scattering indicated that hydrogen was inserted into the material. The amount of hydrogen exceeded the amount attributable to adsorption on the surface. These and other results will be discussed during the presentation. References (1) Kumta, P.N. et al., Adv. Mater ., 2006 , 18, 1178. (2) Djire. A, et al., J. Power Sources, 2015 ,275, 159-166. Figure 1

MXenes

10.1149/ma2015-01/42/2239

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Inexpensive High Fidelity Melt Pool Models in Additive Manufacturing Using Generative Deep Diffusion

Francis Ogoke Quanliang Liu Olabode Ajenifujah Alexander J. Myers Guadalupe Quirarte

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI

High fidelity

Generative model

10.2139/ssrn.4705353

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Unveiling the pseudocapacitive charge storage mechanisms of nanostructured vanadium nitrides using in-situ analyses

Nano Energy (2019)

Abdoulaye Djire Priyanka Pande Aniruddha Deb Jason B. Siegel Olabode Ajenifujah

Vanadium nitride

Pseudocapacitance

Electrochemical energy storage

10.1016/j.nanoen.2019.03.003

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AMGPT: a Large Language Model for Contextual Querying in Additive Manufacturing

arXiv (Cornell University) (2024)

Achuth Chandrasekhar Jonathan H. Chan Francis Ogoke Olabode Ajenifujah Amir Barati Farimani

Generalized large language models (LLMs) such as GPT-4 may not provide specific answers to queries formulated by materials science researchers. These models may produce a high-level outline but lack the capacity to return detailed instructions on manufacturing and material properties of novel alloys. Enhancing a smaller model with specialized domain knowledge may provide an advantage over large language models which cannot be retrained quickly enough to keep up with the rapid pace of research in metal additive manufacturing (AM). We introduce "AMGPT," a specialized LLM text generator designed for metal AM queries. The goal of AMGPT is to assist researchers and users in navigating the extensive corpus of literature in AM. Instead of training from scratch, we employ a pre-trained Llama2-7B model from Hugging Face in a Retrieval-Augmented Generation (RAG) setup, utilizing it to dynamically incorporate information from $\sim$50 AM papers and textbooks in PDF format. Mathpix is used to convert these PDF documents into TeX format, facilitating their integration into the RAG pipeline managed by LlamaIndex. Expert evaluations of this project highlight that specific embeddings from the RAG setup accelerate response times and maintain coherence in the generated text.

10.48550/arxiv.2406.00031

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Integrating Multi-Physics Simulations and Machine Learning to Define the Spatter Mechanism and Process Window in Laser Powder Bed Fusion

Olabode Ajenifujah Francis Ogoke Florian Wirth Jack Beuth Amir Barati Farimani

Process window

Fusion mechanism

10.2139/ssrn.4957778

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Amgpt: A Large Language Model for Contextual Querying in Additive Manufacturing

Achuth Chandrasekhar Jonathan Chan Francis Ogoke Olabode Ajenifujah Amir Barati Farimani

10.2139/ssrn.4870552

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Integrating Multi-Physics Simulations and Machine Learning to Define the Spatter Mechanism and Process Window in Laser Powder Bed Fusion

arXiv (Cornell University) (2024)

Olabode Ajenifujah Francis Ogoke Florian Wirth Jack Beuth Amir Barati Farimani

Laser powder bed fusion (LPBF) has shown promise for wide range of applications due to its ability to fabricate freeform geometries and generate a controlled microstructure. However, components generated by LPBF still possess sub-optimal mechanical properties due to the defects that are created during laser-material interactions. In this work, we investigate mechanism of spatter formation, using a high-fidelity modelling tool that was built to simulate the multi-physics phenomena in LPBF. The modelling tool have the capability to capture the 3D resolution of the meltpool and the spatter behavior. To understand spatter behavior and formation, we reveal its properties at ejection and evaluate its variation from the meltpool, the source where it is formed. The dataset of the spatter and the meltpool collected consist of 50 % spatter and 50 % melt pool samples, with features that include position components, velocity components, velocity magnitude, temperature, density and pressure. The relationship between the spatter and the meltpool were evaluated via correlation analysis and machine learning (ML) algorithms for classification tasks. Upon screening different ML algorithms on the dataset, a high accuracy was observed for all the ML models, with ExtraTrees having the highest at 96 % and KNN having the lowest at 94 %.

Process window

Fusion mechanism

10.48550/arxiv.2405.07823

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AMGPT: A large language model for contextual querying in additive manufacturing

Additive Manufacturing Letters (2024)

Achuth Chandrasekhar Jonathan Chan Francis Ogoke Olabode Ajenifujah Amir Barati Farimani

10.1016/j.addlet.2024.100232

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Double Layer and Pseudocapacitive Charge-Storage Mechanisms in Carbides and Nitrides

Meeting abstracts/Meeting abstracts (Electrochemical Society. CD-ROM) (2014)

Abdoulaye Djire Olabode Ajenifujah Alice Sleightholme Paul G. Rasmussen Levi T. Thompson

Introduction Early transition-metal carbides and nitrides are promising candidates for use in supercapacitor electrodes due to their high electronic conductivities, high surface areas (can exceed 200 m 2 g -1 ), good electrochemical stabilities and high capacitance [1, 2]. For example, the capacitance for VN has been reported to be as high as 1340 Fg -1 in aqueous KOH [3]. This high capacitance has attributed to a combination of electric double-layer formation and faradaic reactions occurring on the nitride or oxynitride (VN x O y ) surface [3]. Despite efforts to date, the nature of the faradaic redox reactions or pseudocapacitive properties of early transition-metal carbides and nitrides remains ill-defined. This presents a challenge to the full exploitation of these materials. Here we report a detailed investigation of the charge-storage mechanisms in early transition-metal carbides and nitrides in aqueous media. The contributions of both double-layer and pseudocapacitive mechanisms have been deconvoluted using a combination of electrochemical techniques including cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS) and electrochemical quartz crystal microbalance (EQCM), x-ray absorption spectroscopy and neutron scattering. Experimental High-surface-area Ti, V, Nb, Mo, and W carbides and nitrides were prepared from their oxide precursors TiO 2 (Alfa Aesar), V 2 O 5 (Alfa Aesar), N 2 O 5 (Alfa Aesar), (NH 4 ) 6 Mo 7 O 24 .4H 2 O (81-83% as MoO 3 , Alfa Aesar) and WO 3 (Alfa Aesar) respectively, by temperature-programmed reaction (TPR) synthesis with 15% CH 4 / H 2 (Cryogenic Gases) or NH 3 (Cryogenic Gases), respectively, then passivated using a flowing mixture of 1% O 2 /He (Cryogenic Gases). Characterization of the structural properties was performed using nitrogen physisorption (BET surface area) and X-ray diffraction. The CV was used to establish the stability windows and capacitances for these materials. The capacitance was deconvoluted into double-layer capacitance and pseudocapacitance by means of CV and EIS. EQCM was used to characterize the nature of the adsorbed/desorbed species during charge/discharge. For selected materials details regarding key species and hydrogen adsorption were determined from the x-ray absorption and neutron scattering results. Results and Discussion Figure 1 shows the response of VN in the frequency range of 10 kHz to 10 mHz in acidic medium for selected potentials within the stable potential window. We observed plateaus at -0.7 and -0.44V bias potentials. This region is believed to be a signature of double-layer and surface adsorption, respectively. This behavior is expected given that both processes depend strongly on accessible surface area. As shown in Figure 2, the double-layer capacitance determined for VN in acidic medium was approximately 96 μFcm -2 . The charge-storage mechanism was found to be a combination of surface redox reaction, adsorption and double-layer charging. Similar examinations have been applied to the other carbides and nitrides listed above in aqueous media and the results will be discussed. References (1) Cladridge, J. B.; York, A. P. E.; Brungs, A. J.; Green Malcolm L. H.; Chem. Mater. 2000 , 12, 132. (2) Wixom, M.R.; Tarnowski, D. J.; Parker, J. M.; Lee , J.Q.; Chen, P. –L.; Song, I.; Thompson, L. T.; Mat. Res. Soc. Symp. Proc. 1998, 496, 643. (3) Choi, D.; Kumta, P. N.; Electrochem. Solid-State Lett. 2005, 8, 8, A418.

MXenes

Double-layer capacitance

10.1149/ma2014-02/3/196

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Modeling Melt Pool Features and Spatter Using Symbolic Regression and Machine Learning

arXiv (Cornell University) (2025)

Olabode Ajenifujah Amir Barati Farimani

Additive manufacturing (AM) is a rapidly evolving technology that has attracted applications across a wide range of fields due to its ability to fabricate complex geometries. However, one of the key challenges in AM is achieving consistent print quality. This inconsistency is often attributed to uncontrolled melt pool dynamics, partly caused by spatter which can lead to defects. Therefore, capturing and controlling the evolution of the melt pool is crucial for enhancing process stability and part quality. In this study, we developed a framework to support decision-making in AM operations, facilitating quality control and minimizing defects via machine learning (ML) and polynomial symbolic regression models. We implemented experimentally validated computational tools as a cost-effective approach to collect large datasets from laser powder bed fusion (LPBF) processes. For a dataset consisting of 281 process conditions, parameters such as melt pool dimensions (length, width, depth), melt pool geometry (area, volume), and volume indicated as spatter were extracted. Using machine learning (ML) and polynomial symbolic regression models, a high R2 of over 95 % was achieved in predicting the melt pool dimensions and geometry features for both the training and testing datasets, with either process conditions (power and velocity) or melt pool dimensions as the model inputs. In the case of volume indicated as spatter, R2 improved after logarithmic transforming the model inputs, which was either the process conditions or the melt pool dimensions. Among the investigated ML models, the ExtraTree model achieved the highest R2 values of 96.7 % and 87.5 %.

Symbolic Regression

10.48550/arxiv.2501.08922

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