Quantum Innovation

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https://hdl.handle.net/10388/14352

Applying science, engineering, and a study of the challenges faced by individuals and societies to design new technologies that leverage the special behaviour of quantum particles can help to make our world a better place.

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The efficacy of oleic acid treatment in passivating MAPbI3 films
(Royal Society of Chemistry, 2025-04) Abdelmageed, Ghada; Kahwagi, Rashad F.; Korkomaz, Joelle; El-Halaby, Anthony; Leontowich, Adam F. G.; Hinds, Sean; Koleilat, Ghada I.
Reliability, scalability, and excellent film properties with large crystals and low grain boundaries are essential for successfully commercializing perovskites in optoelectronic applications. Our previous reports introduced meniscus-guided blade coating, or shearing, which is referred to as one-step blade coating in the present study, as a promising method for depositing scalable perovskite films with millimetre-sized crystals, fulfilling two of the essential criteria. As a subsequent study, we investigated the stability of the films in response to humidity by employing a readily accessible hydrophobic molecule, oleic acid (OA), through surface passivation. We compared the quality of the surface treatment on films produced via one-step and two-step deposition methods utilizing spin and blade coating techniques while subjecting them to continuous exposure to high humidity levels. Initially, we applied OA to the films using spin-coating, which is the standard method for surface passivation. Our results prove that the film properties resulting from the deposition technique determine the effectiveness of the passivation process. A quick surface treatment using OA via spin coating can be highly effective for perovskite films with smooth surfaces and smaller grain sizes, in contrast to textured films with larger crystal sizes. By tailoring the surface treatment method from spin coating to dip coating, we demonstrated that OA can prolong the stability of perovskites for months under continuous high-humidity exposure.
Performance comparison of variable-stepsize IMEX SBDF methods on advection-diffusion-reaction models
(Elsevier, 2024-04-14) Mara'Beh, Raed Ali; Mantas, J.M.; Gonzalez, Pedro; Spiteri, Raymond J
Advection-diffusion-reaction (ADR) models describe transport mechanisms in fluid or solid media. They are often formulated as partial differential equations that are spatially discretized into systems of ordinary differential equations (ODEs) in time for numerical resolution. This paper investigates the performance of variable stepsize, semi-implicit, backward differentiation formula (VSSBDF) methods of up to fourth order for solving ADR models employing two different implicit-explicit splitting approaches: a physics-based splitting and a splitting based on a dynamic linearization of the resulting system of ODEs, called jacobian splitting in this paper. We develop an adaptive time-stepping and error control algorithm for VSSBDF methods up to fourth order based on a step-doubling refinement technique using estimates of the local truncation errors. Through a systematic comparison between physics-based and Jacobian splitting across six ADR test models, we evaluate the performance based on CPU times and corresponding accuracy. Our findings demonstrate the general superiority of Jacobian splitting in several experiments.
Drift-kinetic PIC simulations of plasma flow and energy transport in the magnetic mirror configuration
(AIP Publishing, 2025-03) Tyushev, Mikhail; Smolyakov, Andrei; Sabo, Andy; Groenewald, Roelof Erasmus; Necas, Ales; yushmanov, peter
Plasma flow and acceleration in a magnetic mirror configuration are studied using a drift-kinetic particles-in-cell model in the paraxial approximation, with an emphasis on finite temperature effects and energy transport. Energy conversion between electrons and ions, overall energy balance, and axial energy losses are investigated. The simulations of plasma flow, acceleration, and energy transport in the magnetic mirror are extended into the high-density regimes with implicit particle-in-cell simulations. It is shown that profiles of the anisotropic ion temperatures and heat fluxes obtained with the full drift-kinetic model compare favorably with the results of a fluid model, which includes collisionless ion heat fluxes beyond the two-pressure adiabatic equations. The effects of collisions on trapped electrons and the resulting impacts on electron temperature and electric field profiles are investigated using a model collision operator.
Janus group V1B-based pnictogen-halide monolayers: a new class of multifunctional quantum materials from first-principles predictions
(Royal Society of Chemistry, 2025-03-19) Gusarov, Sergey; Ekuma, Chinedu; Chang, Gap Soo; Alizade, Mina; Naseri, Mosayeb
This study employed density functional theory to discover a new family of 48 two-dimensional Janus monolayers with the formula MXY, where M stands for transition metals (Cr, Mo, or W), X represents a group V element (P, As, Sb, or Bi), and Y denotes a halide (F, Cl, Br, or I). The cohesive energy and phonon dispersion calculations show that most of these materials are energetically and dynamically stable. Subsequently, the thorough investigation into the electrical structure allows the classification of these monolayers as metals (CrPI and WPI) or semiconductors with narrow band gaps ranging from 0.69 to 2.15 eV. Meanwhile, the MoSbBr, MoSbI, and WBiCl monolayers are defined to be able to function as photocatalysts in the water splitting process, and the CrAsCl monolayer exhibits significant potential for valleytronic applications due to its intrinsic valence band splitting of about 90 meV. Finally, significant Rashba splitting was observed near the Γ point in the valence band of Janus MXY monolayers, where the growth in atomic weight (W > Mo > Cr and Bi > Sb > As > P) corresponds to a greater spin–orbit coupling effect on the Rashba parameter. Their Rashba values are comparable to those ofother well-known 2D materials, indicating great potential for spintronic applications. Our findings not only present a broad range of 2D materials, but also highlight their potential for next-generation electrical, photonic, and catalytic technologies.
Approximate conservation laws of partial differential equations with a small parameter
(The Royal Society Publishing, 2025-03) Cheviakov, Alexei; Tarayrah, Mahmood Rajih; Yang, Zhengzheng
Partial differential equations (PDEs) involving perturbation terms with a small parameter often have less analytical structure, in particular, fewer symmetries and conservation laws, compared to the unperturbed PDEs. For such perturbed PDEs, approximate conservation laws can be consistently defined. The set of approximate conservation laws comprises equivalence classes where members of each class differ by a trivial approximate conservation law. Similar to exact ones, approximate conservation laws can be systematically constructed using the characteristic approach with approximate multipliers. Examples of new approximate conservation laws are presented for perturbed nonlinear heat and wave equations. For approximately variational problems, an analogue of the first Noether’s theorem relates approximate multipliers to evolutionary components of approximate local Lie symmetry generators. The multiplier method used to obtain approximate conservation laws includes the Noether approach and generalizes it to a non-variational system. The procedure to use approximate local symmetries to obtain new approximate conservation laws from known ones, in terms of fluxes and multipliers, is established and illustrated. It is shown that approximate conservation laws lead to potential systems that can be used to obtain new approximate potential symmetries of the given PDE system with a small parameter.
On the microstructure and dynamic mechanical behavior of Cu–Cr–Zr alloy manufactured by high-power laser powder bed fusion
(Elsevier, 2025) Azizi, Nadia; Asgari, Hamed; Hasanabadi, Mahyar; Odeshi, Akindele; Toyserkani, Ehsan
This study explores high-power laser powder bed fusion (LPBF) processing of Cu–Cr–Zr alloy, focusing on its high strain rate dynamic mechanical response and microstructural evolution. The alloy undergoes significant strain hardening during dynamic impact loading, primarily attributed to intensified dislocation interactions and multiplication. This is accompanied by thermal softening induced by adiabatic heating, therefore improving strain accommodation. As the strain rate increases from 4400 s−1 to 11300 s−1, the ultimate compressive strength (UCS) enhances from 173 ± 8 MPa to 489 ± 14 MPa, demonstrating a high strain rate sensitivity (SRS) of ∼ 1. Microstructural examinations reveal that higher strain rates intensify the occurrence of adiabatic shear bands (ASBs), leading to severe localized plastic deformation. These ASBs generate localized stress concentrations, which in turn accelerate crack initiation and propagation through pore formation and coalescence within the ASBs. Despite this severe plastic deformation, texture analysis indicates that the crystallographic texture remains largely stable which suggests that the deformation mechanism is primarily governed by dislocation motion and interaction, rather than by crystal structure reorientation. Overall, the alloy balances strain hardening and strain accommodation at high strain rates, making it well-suited for applications requiring strength and resilience under dynamic impacts.
Coarse for Fine: Bounding Box Supervised Thyroid Ultrasound Image Segmentation Using Spatial Arrangement and Hierarchical Prediction Consistency
(IEEE Journal of Biomedical and Health Informatics, 2025) Chi, Jianning; Lin, Geng; Li, Zelan; Zhang, Wenjun; Chen, Jia-hui; Huang, Ying
Weakly-supervised learning methods have become increasingly attractive for medical image segmentation, but suffered from a high dependence on quantifying the pixel-wise affinities of low-level features, which are easily corrupted in thyroid ultrasound images, resulting in segmentation over-fitting to weakly annotated regions without precise delineation of target boundaries. We propose a dual-branch weakly-supervised learning framework to optimize the backbone segmentation network by calibrating semantic features into rational spatial distribution under the indirect, coarse guidance of the bounding box mask. Specifically, in the spatial arrangement consistency branch, the maximum activations sampled from the preliminary segmentation prediction and the bounding box mask along the horizontal and vertical dimensions are compared to measure the rationality of the approximate target localization. In the hierarchical prediction consistency branch, the target and background prototypes are encapsulated from the semantic features under the combined guidance of the preliminary segmentation prediction and the bounding box mask. The secondary segmentation prediction induced from the prototypes is compared with the preliminary prediction to quantify the rationality of the elaborated target and background semantic feature perception. Experiments on three thyroid datasets illustrate that our model outperforms existing weakly-supervised methods for thyroid gland and nodule segmentation and is comparable to the performance of fully-supervised methods with reduced annotation time. The proposed method has provided a weakly-supervised segmentation strategy by simultaneously considering the target's location and the rationality of target and background semantic features distribution. It can improve the applicability of deep learning based segmentation in the clinical practice. The source code and relative datasets will be available at https://github.com/LanLanUp/SAHP-Net.
High-Sensitivity and Flexible Motion Sensing Enabled byRobust, Self-Healing Wood-Based Anisotropic Hydrogel Composites
(Small, 2025-02) Teng, Youchao; Zhang, Zhilei; Cui, Yunqi; Su, Zhe; Godwin, Matthew; Chung, TzuChun; Zhou, Yongzan; Leontowich, Adam F. G.; Islam, Muhammad Shahidul; Tam, Kam C.; Wu, Yimin A.
By integrating polyvinyl alcohol (PVA)-borate-tannic acid (TA)-sodium sulfate into cellulosic wood matrices, a novel wood-basedPVA-borate-TA-sodium sulfate (WPBTS) hydrogel is successfully synthesized. Through a multicomponent synergistic design combining natural lignocellulose, PVA, borax, TA, and sodium sulfate, multiple dynamic cross-linking mechanisms—dynamic borate bonding, hydrogen bonding, and metal-ligand interactions—are established, resulting in WPBTS hydrogels with exceptional mechanical properties and self-healing capabilities. The mechanical strength of the WPBTS hydrogel reached an impressive 19.8 MPa, a 45-fold increase compared to PVA-borax-tannic acid (PBTS) hydrogels. Furthermore, the assembled WPBTS hydrogel-based flexible sensor demonstrates a remarkably fast response time of just 20 ms and maintains excellent performance in challenging simulated saline environments. This innovation represents a significant advancement in sensor technology and highlights the potential for transformative applications in complex and demanding scenarios.
Machine learning models for predicting interaction affinity energy between human serum proteins and hemodialysis membrane materials
(Scientific Reports, 2025-01) Nazari, Simin; Abdelrasoul, Amira
Membrane incompatibility poses significant health risks, including severe complications and potential fatality. Surface modification of membranes has emerged as a pivotal technology in the membrane industry, aiming to improve the hemocompatibility and performance of dialysis membranes by mitigating undesired membrane-protein interactions, which can lead to fouling and subsequent protein adsorption. Affinity energy, defined as the strength of interaction between membranes and human serum proteins, plays a crucial role in assessing membrane-protein interactions. These interactions may trigger adverse reactions, potentially harmful to patients. Researchers often rely on trial-and-error approaches to enhance membrane hemocompatibility by reducing these interactions. This study focuses on developing machine learning algorithms that accurately and rapidly predict affinity energy between novel chemical structures of membrane materials and human serum proteins, based on a molecular docking dataset. Various membrane materials with distinct characteristics, chemistry, and orientation are considered in conjunction with different proteins. A comparative analysis of linear regression, K-nearest neighbors regression, decision tree regression, random forest regression, XGBoost regression, lasso regression, and support vector regression is conducted to predict affinity energy. The dataset, comprising 916 records for both training and test segments, incorporates 12 parameters extracted from data points and involves six different proteins. Results indicate that random forest (R² = 0.8987, MSE = 0.36, MAE = 0.45) and XGBoost (R² = 0.83, MSE = 0.49, MAE = 0.49) exhibit comparable predictive performance on the training dataset. However, random forest outperforms XGBoost on the testing dataset. Seven machine learning algorithms for predicting affinity energy are analyzed and compared, with random forest demonstrating superior predictive accuracy. The application of machine learning in predicting affinity energy holds significant promise for researchers and professionals in hemodialysis. These models, by enabling early interventions in hemodialysis membranes, could enhance patient safety and optimize the care of hemodialysis patients.
Accurate and Efficient Numerical Simulation of Land Models Using SUMMA With SUNDIALS
(Wiley, 2024-11-27) Spiteri, Raymond John; Van Beusekom, Ashley; Klenk, Kyle; Zolfaghari, Reza; Trim, Sean; Knoben, Wouter; Ireson, Andrew; Clark, Martyn
Numerical simulation of land models without error control can be highly inaccurate. We present the incorporation of the Suite of Nonlinear and Differential-Algebraic Equation Solvers (SUNDIALS) package to solve the equations that simulate thermodynamics and hydrologic processes in the Structure for Unifying Multiple Modeling Alternatives (SUMMA) land model. The algorithmic features of SUNDIALS, such as error estimation and adaptive order and step-size control, result in a SUMMA-SUNDIALS model that delivers substantially improved accuracy and relative computational efficiency compared to integration with the previous SUMMA model, which uses the low-order backward Euler method with no rigorous error control. The results are demonstrated through simulations over the North American continent with more than 500,000 spatial elements. Compared to the previous SUMMA model, we find that the simulations produced by the SUMMA-SUNDIALS model are orders of magnitude closer to converged solutions for the same computational cost. Being able to efficiently perform more reliable simulations makes the SUMMA-SUNDIALS model a powerful tool for improving our understanding of the terrestrial component of the Earth System.
A comparative study of the long-term aqueous durability of brannerite (Ce0.94Ti2O6–δ) and glass-brannerite (Fe-Al-BG-Ce0.94Ti2O6–δ) composite materials
(Applied Surface Science, 2025-01) Mikhchian, Mehrnaz; Grosvenor, Andrew
The long-term aqueous corrosion behavior of Fe-Al borosilicate glass-brannerite (Fe-Al-BG- Ce0.94Ti2O6–δ) composite material as a potential nuclear wasteform has been investigated to understand how the corrosion behavior of these materials can be compared to current nuclear wasteforms (i.e., borosilicate glass). It was found that the aqueous corrosion behavior of the Fe-Al-BG-Ce0.94Ti2O6–δ composite material resulted from a combined corrosion behavior of the individual Fe-Al-BG and Ce0.94Ti2O6–δ phases when these materials were exposed to deionized water. A combination of surface and bulk analyses has demonstrated that the surface composition and chemistry of Ce0.94Ti2O6–δ and Fe-Al-BG-Ce0.94Ti2O6–δ composite materials were affected by aqueous corrosion, whereas the long-range (i.e., bulk) structure of these materials remained stable over 365 days of exposure to deionized water. This study has shown that the corrosion resistance of Fe-Al-BG-Ce0.94Ti2O6–δ composite material is comparable to Fe-Al-BG, which suggests that this composite material could be further investigated as a potential substitute for borosilicate glass nuclear wasteforms.
Characterizing Structure and Electrochemical Properties of Advanced Si/C Anode Materials
(Journal of The Electrochemical Society, 2025-01) Rathore, Divya; Abraham, Jeffin James; Mendel-Elias, Eytan; Li, Zhifei; Zaker, Nafiseh; Amirkhiz, Babak Shalchi; Michel, Johnson; Hamam, Ines; Leontowich, Adam; Bond, Toby; Dahn, Jeff
The increasing commercial interest in silicon-based anode materials for Li-ion batteries has driven the development of advanced structural designs to address the challenges of poor cycling stability. This study examines the structure of commercial silicon/carbon composite materials where nano silicon clusters are embedded within a carbon matrix. The size of silicon and carbon nanoclusters is determined by comparing experimental X-ray diffraction patterns with calculated patterns based on the Debye scattering formalism, as implemented in the program DEBUSSY. The size, morphology, surface areas, and porosities of the carbon matrix and composite are measured, along with their resulting tap and true densities. Their electrochemical performance is also assessed to determine operando stack growth and cycling stability. By restricting silicon cluster sizes to sub-nanometer dimensions within a porous carbon matrix, a low specific surface area can be achieved along with a specific capacity of ∼2000 mAh g−1. Additionally, this approach results in high tap density values close to 1 g cc−1, reduces reversible stack growth, and minimizes irreversible stack growth caused by particle cracking during volume changes, thereby significantly enhancing the overall stability and performance of the anode material.
Internal Lagrangians and spatial-gauge symmetries
(International Press of Boston, 2024) Druzhkov, Kostya
A direct reformulation of the Hamiltonian formalism in terms of the intrinsic geometry of infinitely prolonged differential equations is obtained. Concepts of spatial equation and spatial-gauge symmetry of a Lagrangian system of equations are introduced. A noncovariant canonical variational principle is proposed and demonstrated using the Maxwell equations as an example. A covariant canonical variational principle is formulated. The results obtained are applicable to any variational equations, including those that do not originate in physics.
The impact of non-uniformity and resistivity on the homogenised corrosion parameters of rebars in concrete – a circuit model analysis
(Corrosion Engineering, Science and Technology: The International Journal of Corrosion Processes and Corrosion Control, 2023-06) Li, Gang; Evitts, Richard; Boulfiza, Moh
When rebar corrosion parameters are characterised from an electrochemical polarisation curve, the non-uniform rebar surface conditions need to be considered. In this research, a circuit model was developed to simulate the polarisation behaviour of rebar in concrete. It is found that the resistivity of concrete leads to non-uniform potential on the rebar, which causes the polarisation curve of the entire rebar to deviate from the Butler–Volmer kinetics. This, in turn, leads to an overestimation of the Tafel constants and the corrosion current density. Such deviations are more pronounced with higher concrete resistivity, especially when the active and passive rebar surfaces have a similar area ratio. The study recommends using potentiodynamic scans of representative reinforced concrete samples of the field conditions or the calculated parameters using an averaging technique, such as the proposed circuit model, to obtain accurate E-I curves or parameters for electrochemical modelling and corrosion rate prediction.
Extrusion bioprinting from a fluid mechanics perspective
(ACCSCIENCE, 2024-08-30) Gharraei, Reza; Bergstrom, Donald; Chen, Xiongbiao (Daniel)
Bioprinting is an emerging technology for fabricating intricate and diverse structures that closely mimic natural tissues and organs for such applications as tissue engineering, drug delivery, and cancer research as well. Among the various bioprinting techniques, extrusion-based bioprinting stands out due to its capability to apply a wide range of biomaterials and living cells and its controllability over printed structures. In bioprinting, the bioink stored in a syringe is forced to flow through the nozzle connected to the syringe, and then to exit and deposit onto the printing stage to form three-dimensional (3D) structures. The bioprinting process involves the flow of bioink in both syringe and nozzle and then its flow or spreading on a printing stage. As a result, fluid mechanics plays a crucial role in extrusion bioprinting. Notably, the biomaterials used in bioprinting are typically non-Newtonian fluids, which have complex viscoelastic and thixotropic behaviors; and the influence of these behaviors on the bioprinting process has been drawn considerable attention by employing various methods, including the numerical simulations via computational fluid dynamics (CFD). This paper reviews the latest development in the fluid mechanics aspects of extrusion-based bioprinting to shed light on the challenges and key considerations involved. It covers the topics of extrusion bioprinting (including driving mechanisms, printability, cell viability), biomaterial rheology and its effect on bioprinting, multi-material bioprinting and numerical simulation of bioprinting. Key issues and challenges are also discussed along with the recommendations for future research.
Conditional cross-correlation analysis of floating potential fluctuations in the TJ-II stellarator
(Radiation Effects and Defects in Solids, 2024-12-31) Bsharat, H Voldiner, Igor van Milligen, B. Ph Xiao, C
The conditional nonlinear cross-correlation technique has been used to analyze the floating potential fluctuations measured by a radially distributed rake probe array during the electrode biasing experiments on the TJ-II stellarator. Preliminary results suggest that the propagation direction of the turbulence energy changes from outwards in the case without biasing to inwards when biasing is applied.
The Transformative Potential of Artificial Intelligence for Public Sector Reform
(Canadian Public Adminstration, 2024) Longo, Justin
This article examines the experience with and potential application of artificial intelligence (AI) within the Canadian public service. Assessed are the ways in which AI is being applied to internal administration and operations, the bilingual requirements of Canada's federal government, public service delivery, policy analysis and advising, application adjudication, and monitoring and regulatory compliance. The response to date from the federal government on how to guide the use of AI in the public service is assessed, and options and prospects for the future are offered in conclusion.
All 81 crepant resolutions of a finite quotient singularity are hyperpolygon spaces
(AMS: Journal of Algebraic Geometry, 2024) Bellamy, Gwyn; Craw, Alastair; Rayan, Steven; Schedler, Travis; Weiß, Hartmut
Surface-Modified Chitosan: An Adsorption Study of a “Tweezer-Like” Biopolymer with Fluorescein
(MDPI, 2019-08-18) Vafakish, Bahareh; Wilson, Lee
Tweezer-like adsorbents with enhanced surface area were synthesized by grafting aniline onto the amine sites of a chitosan biopolymer scaffold. The chemical structure and textural properties of the adsorbents were characterized by thermogravimetric analysis (TGA) and spectral methods, including Fourier transform infrared (FT-IR), nuclear magnetic resonance (1H- and, 13C-NMR) and scanning electron microscopy (SEM). Equilibrium solvent swelling results for the adsorbent materials provided evidence of a more apolar biopolymer surface upon grafting. Equilibrium uptake studies with fluorescein at ambient pH in aqueous media reveal a high monolayer adsorption capacity (Qm) of 61.8 mg·g−1, according to the Langmuir isotherm model. The kinetic adsorption profiles are described by the pseudo-first order kinetic model. 1D NMR and 2D-NOESY NMR spectra were used to confirm the role of π-π interactions between the adsorbent and adsorbate. Surface modification of the adsorbent using monomeric and dimeric cationic surfactants with long hydrocarbon chains altered the hydrophile-lipophile balance (HLB) of the adsorbent surface, which resulted in attenuated uptake of fluorescein by the chitosan molecular tweezers. This research contributes to a first example of the uptake properties for a tweezer-like chitosan adsorbent and the key role of weak cooperative interactions in controlled adsorption of a model anionic dye.
Bounds on 𝑎_𝜇^(HVP,LO) using Hölder's inequalities and finite-energy QCD sum rules
(Elsevier, 2024-09-26) Li, Siyuan; Steele, Tom; Ho, Jason; R-Rahaman, Raza; Williams, K.; Kleiv, Robin
This study establishes bounds on the leading-order (LO) hadronic vacuum polarization (HVP) contribution to the anomalous magnetic moment of the muon (𝑎_𝜇^(HVP,LO), 𝑎𝜇 = (𝑔 − 2)𝜇∕2) by using Hölder’s inequality and related inequalities in Finite-Energy QCD sum rules. Considering contributions from light quarks (𝑢, 𝑑, 𝑠) up to five-loop order in perturbation theory within the chiral limit, leading-order light-quark mass corrections, next-to-leading order for dimension-four QCD condensates, and leading-order for dimension-six QCD condensates, the study finds QCD lower and upper bounds as (657.0 ± 34.8) × 10−10 ≤ 𝑎_𝜇^(HVP,LO) ≤ (788.4 ± 41.8) × 10−10.

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