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Nanotechnology-enabled energy efficiency in semiconductors: plasmon-induced super-semiconductors and ballistic transport devices
No full textThis article was originally published in Frontiers in Nanotechnology. The version of record is available at https://doi.org/10.3389/fnano.2025.1560733
© 2025 Li and Wei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are
credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.The semiconductor industry consumes staggering amounts of electricity annually, surpassing the energy usage of over 100 nations. This immense consumption not only underscores the environmental impact but also generates substantial heat within semiconductor devices, adversely affecting their performance, lifespan, and reliability, posing significant challenges to the advancement of nanodevices. To address these challenges, reducing energy consumption through the use of advanced, energy-efficient technologies has become a priority. Energy-efficient electronics (EEE), enabled by nanotechnology, have the potential to drastically reduce energy consumption in semiconductor devices while simultaneously enhancing their performance. From this perspective, this discussion focuses on two nano-semiconductor technologies poised to advance EEEs: plasmon-induced metal-based semiconductors and ballistic transport in nanostructured semiconductors. For example, p-n junction diodes constructed with the metal-based semiconductors can reduce power consumption by 3-4 orders of magnitude compared with silicon-based devices due to their low resistivity; similarly, the excellent ballistic transport property of InSe FETs enables an energy-delay product of ∼4.32*10−29 Js/μm of the devices, one order of magnitude lower than the Si counterparts. This perspective examines the offerings of each of these disciplines and explores how nanotechnology can be utilized to conserve energy and enhance performance. Differences from traditional technologies and limitations in existing research will also be assessed.The author(s) declare that financial support was received for the research and/or publication of this article. ZL is grateful for the financial support from the National Natural Science Foundation of China (Grants No. 52371197, 51671139) and the Natural Science
Foundation of Zhejiang Province (Grant No. LY21F050001)
Non-muscle Myosin IIA (NMIIA) regulates mouse lens cellular differentiation during development and cataract formation
No full text"At the request of the author or degree granting institution, this graduate work is not available to view or purchase until April 15 2026"--ProQuest abstract/details page.Fowler, Velia M.The ocular lens is a transparent tissue in the anterior chamber of the eye that is responsible for focusing light onto the retina for clear vision. A common condition with lens aging is cataracts, defined as opacities in the lens. The lens grows throughout our lifetime, making it a unique model system to study tissue morphogenesis & age-dependent diseases. The lens comprises a monolayer of epithelial cells covering the anterior hemisphere and a bulk mass of fiber cells. Lens epithelial cells (LECs) proliferate in the germinative zone and then migrate to the equator. At the equator, the LECs transform from randomly packed cells to precisely aligned and hexagon-shaped meridional row (MR) cells. The MR cells then differentiate into fiber cells that retain the precise alignment, hexagon shape, and packing from their precursor MR cells. The exact mechanisms through which these cell shapes and packing transformations occur at the lens equators are poorly understood. ☐ Non-muscle myosin IIs (NMIIs) are motor proteins that self-associate to form bipolar filaments and pull actin filaments (F-actin) to generate contractile forces. In other systems, actomyosin contractility regulates cell shape changes, ordered packing, migration, and cytokinesis. MYH9-related disease (MYH9-RD) is an autosomal dominant genetic disorder caused by mutations in the MYH9 gene that encodes NMIIA heavy chain. Patients with MYH9 mutations present with bleeding disorders but can also exhibit kidney disease, liver disease, hearing loss, and presenile cataracts. The three most common MYH9 mutations are R702C (located in motor domain, reducing ATP hydrolysis and F-actin translocation), D1424N (located in rod domain, affecting actomyosin interactions), and E1841K (located in rod domain, impairing NMIIA bipolar filament assembly). Full-body genetic knock-in mice with these mutations have been previously generated by Robert S. Adelstein at the National Heart, Blood, and Lung Institute. These knock-in mice provide us with an opportunity to examine the role of NMIIA in lens development, cellular morphogenesis, and cataract formation. ☐ Therefore, I initially evaluated lens transparency in 2-month-old control mice and mice with Myh9 mutations (R702C, D1424N, and E1841K). Mice homozygous for R702C and D1424N mutations do not survive. At 2 months of age, both control and mutant (R702C, D1424N, and E1841K) mice exhibited normal-sized transparent lenses. Next, I examined the 2-month-old mouse lenses for cellular defects by performing lens whole mount and/or immunofluorescence confocal microscopy of lens sections. NMIIAGFP-R702C/+ mice exhibit ordered packing suggesting either endogenous-NMIIA allele is sufficient or NMIIB can compensate for mutant motor domain function. NMIIAD1424N/+, NMIIAE1841K/+, and NMIIAE1841K/E1841K mice exhibit irregular fiber cell organization. As NMIIA is predominantly expressed in LECs, I hypothesized that the fiber cell disorder arises due to the irregular packing of MR cells. I examined MR cell alignment, shape, and packing organization and discovered that NMIIAD1424N/+ mice exhibit mild irregular packing defects. MR cells of NMIIAE1841K/E1841K mice display misalignment, aberrant cell shape, and irregular packing. While 92% of NMIIA+/+ MR cells have six neighbors, ~80% of the NMIIAE1841K/E1841K MR cells have six neighbors and 17% of the NMIIAE1841K/E1841K MR cells have five or seven neighbors. These data suggest that NMIIA rod domain function is critical in forming and/or maintaining the hexagonal packing of MR and fiber cells. ☐ Because NMIIAE1841K/E1841K mice exhibit the most defect in hexagonal packing compared to any other mutants, I focused on the NMIIA-E1841K mutation to further examine the role of NMIIA in MR cell hexagonal patterning. Immunofluorescence microscopy of MR cells demonstrates increased enrichment of NMIIA, N-cadherin, and vinculin at AP-oriented sides of NMIIA+/+ MR cells, but equal distributions on all sides of NMIIAE1841K/E1841K MR cells. Further, F-actin is uniformly distributed around all edges of NMIIA+/+ MR cells but reduced at the AP-oriented edges of NMIIAE1841K/E1841K MR cells. Employing Bayesian Mechanical Inference, we discovered that MR cells in NMIIA+/+ lenses exhibit an anisotropic junctional tension, in which relative tension is more concentrated at the anterior-posteriorly (AP) oriented edges. In contrast, MR cells in NMIIAE1841K/E1841K lenses show isotropic junctional tension on all sides. Together, our data suggests that the NMIIA-E1841K mutation results in altered F-actin, NMIIA, N-cadherin, and vinculin distributions, disrupting the anisotropic orientational pattern of mechanical forces within the tissue, leading to disordered cell packing during mouse lens epithelial cell differentiation. So far, we have introduced a novel function of NMIIA during mouse lens cellular differentiation. ☐ As MYH9 mutations cause cataracts in humans and 2-month-old mutant mice (R702C, D1424N and E1841K) are transparent, we examined cataract incidences in aged control and mutant mice (8 months old) to determine if the mutations cause age-dependent cataracts. Lenses from 8-month-old NMIIAD1424N/+ and NMIIAE1841K/E1841K mice exhibit anterior opacity while lenses from NMIIA+/+, NMIIAGFP-R702C/+ and NMIIAE1841K/+ mice remain transparent. In particular, the NMIIAE1841K/E1841K mice exhibit a rare and highly understudied cataract named anterior polar pyramidal cataracts. Consequently, I used lenses from NMIIAE1841K/E1841K mice as a model to study the mechanisms driving the anterior polar pyramidal cataracts. ☐ First, I examined lens cellular morphology and structure from 8-month-old NMIIA+/+ and NMIIAE1841K/E1841K mice by performing whole mount. In NMIIA+/+ mice, the lens epithelial cells appear as a single layer of tightly connected polygonal flat cells that show apical-basal polarity. In addition, the lens capsule (extracellular matrix) appears as a continuous smooth structure located directly above the lens epithelial cells in the anterior region of the NMIIA+/+ lenses. On the contrary, in the cataract region of the NMIIAE1841K/E1841K lenses, I observe multiple layers of fibroblasts or mesenchymal cells that do not adhere and lack apical-basal polarity. Furthermore, the capsule appears highly irregular in the cataract region of the NMIIAE1841K/E1841K lenses. Gene expression suggests that the NMIIAE1841K/E1841K lenses with cataracts undergo epithelial-to-mesenchymal transition (EMT), in which the cells lose their epithelial marker and upregulate mesenchymal markers, further confirming the cellular phenotype observation. In addition, NMIIAE1841K/E1841K lenses with cataracts exhibit significantly decreased collagen IV (a marker for the lens capsule) expression, while upregulating fibronectin 1 (an extracellular matrix protein; typically not present in the native lens capsule), indicating that the lens capsule structure and composition are being remodeled in the mutant lenses with cataracts. This study uncovers a new role of NMIIA in EMT, thus opening up a new research area for defining the mechanistic basis of how the NMIIA-E1841K mutation results in initiation of EMT. ☐ Overall, my work highlights the function of NMIIA in lens epithelial cell differentiation, in the context of lens development and cataract formation with aging. Further, in addition to lens biology, my research findings provide new directions for investigations in epithelial-to-mesenchymal transition, a key cellular behavior of general importance, that is relevant to the understanding of other pathologies such as cancer.University of Delaware, Department of Biological SciencesPh.D
THE INFLUENCE OF MECHANICAL PROPERTIES ON CARTILAGE SUPERLUBRICITY
No full textenterArticular cartilage supports near frictionless joint movement over a lifetime of hundreds
of millions of articulation cycles because of a complex interaction between its mechanical
properties and tribomechanics. Only recently has cartilage’s unmatched superlubricating
frictional capacity been replicated on benchtop, using the convergent stationary contact area
(cSCA) explant testing configuration(in the presence of synovial fluid). However, the
relationship between cartilage mechanical properties and the tissues capacity for superlubricity
remains unclear. In articular cartilage, the superficial zone and the progressively stiffer middle
and deep zones create a depth-dependent mechanical property gradient that supports load
distribution and maintains fluid pressurization, both thought critical for superlubricity. Changes
in this zonal structure may compromise lubrication, though the precise relationship between
depth-varying mechanics and frictional behaviors remains unknown. Osteoarthritis, a
degenerative joint disease, is characterized by collagen network degeneration, proteoglycan loss,
and cartilage swelling, which alter the tissue’s mechanical properties. To better understand the
relationship between mechanical properties and cartilage lubricity, cSCA configured
osteochondral explants were free swollen in baths of varying tonicity (isotonic and hypotonic;
400 to 55mOsm) before being mechanically characterized via indentation and tribologically
characterized in the cSCA. Overall, this thesis aimed to investigate the effect of osmotically
induced, and reversible changes in bulk and depth-dependent mechanical properties and how (if
at all) they influence cartilage superlubricity.
In Aim I, a novel micro-indentation protocol was developed to evaluate effective contact
moduli at varying tissue depths and indentation speeds, allowing for analysis of zonal
mechanical properties of articular cartilage when subjected to free swelling in varying tonicities
(isotonic and hypotonic; 400 to 55mOsm). These results were compared with macro-indentation
derived “bulk” material properties. The study revealed that near surface measures of effective
moduli at both fast and slow indentation rates appear more sensitive to hypotonic bath influences
than bulk mechanical properties, indicating localized, tonicity (hypoosmolality)-induced
mechanical changes. Such findings indicated that bulk cartilage stiffening is also associated with
localized mechanical changes within the tissue (i.e., near surface stiffening) that may impact its
ability to sustain a low-friction performance.
In Aim 2, cartilage explant samples were subjected to a speed sweep tribological
characterization in the presence of PBS and hyaluronic acid (HA) to determine if there exist
relationship(s) between cartilage stiffening and frictional behavior. Cartilage explant tribology
testing, under the cSCA configuration, showed that cartilage lubricity remained largely
unaffected by hypotonic-driven tissue stiffening. Interestingly, correlations between cSCA
friction coefficients and indentation-based mechanical properties were lubricant and
speed-dependent, underscoring complex surface and hydration interactions. This finding
suggests that cartilage’s frictional behavior is influenced by both the lubrication environment and
sliding speed, highlighting the dynamic nature of cartilage mechanics and its capacity for
low-friction performance under varying physiological conditions.ente
Novel reductive processes for the rapid and complete destruction of munition compounds in wastewater
No full textChiu, Pei C.The U.S. Army has been transitioning to insensitive munition formulations such as IMX-104, which contains the munition compounds (MC) 3-nitro-1,2,4-triazol-5-one (NTO), 2,4-dinitroanisole (DNAN), and 1,3,5-trinitro-1,3,5-triazine (RDX). These MC pose significant challenges for wastewater treatment. Conventional technologies such as biodegradation and granular activated carbon are ineffective at removing these MC, particularly NTO, whereas chemical methods such as nano-zero-valent iron and Fenton are expensive and require low pH. In this study, we evaluated two novel methods for their ability to reductively degrade the MC in a synthetic IMX-104 wastewater: one based on ferrous ion (Fe2+) and the other on dithionite as reductant. Micro-zero-valent iron (μZVI) was also evaluated for comparison. All three processes completely removed the three MC in seconds (Fe(II) and dithionite) to 60 minutes (μZVI). NTO and DNAN were reduced to their amino counterparts, ATO (44%-77%) and DAAN and 2-ANAN (16–72%), respectively. Dithionite was effective at all pH tested (2.8 - 11), and the final pH was close to neutral. Aqueous Fe2+, activated by either hematite or base (NaOH), degraded MC auto-catalytically under neutral to mildly alkaline conditions (8.0 – 10.0). pH control was necessary to sustain the reactivity of μZVI ( 7), but not of dithionite. The proposed methods are destructive, rapid, simple, inexpensive, and environmentally benign. They represent potentially superior options for the destruction of insensitive and legacy MC(e.g., TNT) in wastewater treatment and/or demilitarization efforts at DoD facilities.University of Delaware, Department of Civil, Construction and Environmental EngineeringM.C.E
Access to ınformation and social solidarity in the 2023 Turkey earthquake: disaster education as citizenship education
No full textThis article was originally published in Humanities and Social Sciences Communications. The version of record is available at: https://doi.org/10.1057/s41599-025-04707-0.
© The Author(s) 2025.
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.This qualitative study explores the experiences of 16 survivors of the 2023 earthquake in Turkey, aiming to highlight the critical role of disaster education within broader citizenship education. Through semi-structured interviews and inductive thematic analysis, four key themes emerged: access to information, trust in information sources, social solidarity, and the fulfillment of basic needs. These findings underscore the importance of integrating disaster education into citizenship education to empower individuals with the knowledge and skills necessary for effective disaster preparedness and response. The research advocates for multi-faceted approaches to disaster readiness that not only enhance immediate survival and recovery but also foster long-term community resilience. By amplifying the voices of earthquake survivors, this study contributes to a deeper understanding of the vital intersection between education, citizenship, and disaster management, offering insights into how to better equip citizens to respond to and recover from crises
Prediction of nonlinear wave statistics using machine learning models trained on wave-resolving nearshore hydrodynamics models
No full textHsu, Tian-JianThe modeling of nearshore hydrodynamics demands extraordinary computational power to resolve vastly different scales and processes of interest. To remain tractable, models make trade-offs in what to resolve, at what scale to model, and which processes are parameterized, estimated, or neglected. Wave-averaging represents one such trade-off, whereby variation on the temporal order of a wave period is neglected to instead model properties of the wave field at large. This contrasts with the computationally more demanding wave-resolving models, whereby the time-varying motion of waves within individual wave periods is captured. ☐ Due to their time-averaged nature, wave-averaged models cannot inherently model nonlinear evolution in wave shape in the same way that wave-resolving models can. Such changes are often characterized by the higher-order statistics of skewness and asymmetry. Such higher-order quantities are crucial to understanding complex, coupled processes, such as morphodynamics and sediment transport. However, the computational cost of wave-resolving Boussinesq models makes their application to long time scales and complex coupling formulations impractical for many applications, such as morphodynamics. Consequently, it is difficult to leverage the power of many nonlinear wave models at longer time scales. ☐ The development of scalable machine learning (ML) algorithm has provided new opportunities to bridge scale gaps and couple models. In this vein, the wave-resolving Boussinesq model FUNWAVE-TVD was first validated on experimental data and run upwards of 20,000 times on different representative beach profiles and wave spectra from Duck, North Carolina to generate a corpus of training data to model skewness and asymmetry in the cross-shore. ☐ Principal component analysis (PCA) was used to characterize the variability of the input and output spaces, identify outliers in the dataset, and fit a baseline regression model to capture simple linear dependencies in a low-rank space. This analysis also reveals the hidden relationships between wave skewness and offshore wave spectral as well as wave asymmetry and bathymetry, providing insight into more effective nonlinear ML modeling. Additional machine learning models exploiting nonlinear mappings to low-dimensional latent spaces via kernels were also applied. An encoder-decoder architecture based on a convolutional neural network (CNN) was implemented to capture spatial patterns in the bathymetry and sequential structure in the spectra. Furthermore, decision-tree-based models, including random forest regressors and XGBoost, were fit due to their effectiveness in modeling highly nonlinear relationships. All models demonstrated robustness to overfitting with appropriate hyperparameter tuning, suggesting that low-rank representations and machine-learning-based surrogate models can effectively replicate complex nearshore statistics derived from wave-resolving models in a computationally efficient manner.University of Delaware, Department of Civil, Construction and Environmental EngineeringM.C.E
Towards designer strain distributions in two-dimensional materials
No full text"At the request of the author or degree granting institution, this graduate work is not available to view or purchase until April 27 2026"--ProQuest abstract/details page.Wang, XiThe first demonstration of graphene's superior electronic properties in 2004 sparked an extensive amount of research into the behavior of two-dimensional materials (2D) and their applications. As of the year 2022, over 6,000 types of 2D materials have been discovered and led to over 50,000 publications. Within this family can be found a wide range of materials, from insulators and semiconductors to ferromagnets and topological insulators, with applications across electronics, photonics, and quantum information science. ☐ Recent efforts have been devoted to expanding this diverse set of 2D material properties and applications even further. Among the developed methods, strain engineering has quickly become one of the most promising. The reduced dimensionality and superior mechanical properties of 2D materials enable significant modulation of the bandstructure via mechanical deformation, giving rise to a range of material-dependent effects, from increased carrier mobility to the generation of pseudomagnetic fields. While significant progress has been made in our capabilities to control strain in 2D materials, current methods do not exhibit the deterministic strain control required for many proposed devices. ☐ In this dissertation, we contribute to the ongoing efforts towards attaining deterministic control of strain in 2D materials by leveraging the in-plane strain that arises through out-of-plane deformation. We first introduce a fabrication method to produce silicon oxide nanostructures (probes) with angled sidewalls and deterministic placement to be used as tools in the local strain engineering of 2D materials. The probes are then implemented in the static and non-uniform strain engineering of 2D materials using patterned substrates. We focus on strain engineering of the van der Waals semiconductors gallium selenide (GaSe) and tungsten \ch{WS2} and find that careful design of the nanostructures enables precise control of the in-plane strain in locally-suspended regions transferred flakes. We further show that complex strain distributions can be predicted using finite element analysis and verified experimentally through micro-photoluminescence (PL) and Raman scattering mapping. ☐ We then present a novel platform for dynamically engineering local strain in suspended 2D materials via nano-indentation. Central to our approach is the design and fabrication of a silicon-on-insulator (SOI) based micro-spring (MS) with patterned nanoscale probes at its apex. While AFM probe-based indentation induces in-plane biaxial strain at the point of contact, control over the probe geometry introduces an additional degree of freedom to tune the local strain distribution. We demonstrate this concept using a ring-shaped probe to induce strain in suspended trilayer \ch{WS2} and use finite element analysis to understand the arising strain distributions. Simulations reveal that the ring probe induces nearly uniform biaxial strain across the ring diameter. Experimentally, the arising strain manifests as a measurable shift in the photoluminescence spectra and is found to be reversible. We further show that various strain distributions can be designed within the finite element framework and provide examples of probes that can be used to induce point-like, uniaxial, biaxial, and triaxial strain distributions. ☐ While the presented methodologies greatly advance our capabilities to engineer strain distributions in two-dimensional materials, we have only begun to understand the extent to which strain can be controlled deterministically using these platforms.University of Delaware, Department of Materials Science and EngineeringPh.D
Multi-modal data, deep learning, clustering, predictive modeling, type 2 diabetes, dementia, clustering
No full textBeheshti, RahmatollahWearable sensors are increasingly utilized in healthcare to collect physiological and clinical data, enabling various tasks to enhance diagnosis, prognosis, and intervention strategies. Deep-learning models play a crucial role in processing such data due to their ability to extract complex features. However, many existing approaches focus on single-modality data, making them insufficient for scenarios where static and time-series data coexist. This dissertation presents novel deep-learning solutions for multi-modal healthcare data, with a focus on wearable sensors, addressing predictive and clustering problems. ☐ In the predictive tasks, I developed a neural network that integrates static demographic and lab data with time-series measurements from wearable sensors to forecast the progression of type 2 diabetes. Building upon this, I designed an advanced deep-learning model that leverages both convolutional and recurrent layers to capture intra- and inter-sensor dependencies, enabling a more accurate understanding of dynamic patterns in time-series data. This model significantly improves the prediction of diabetes-related outcomes by extracting and combining patterns from diverse sensor modalities. ☐ In clustering tasks, I proposed a supervised method to classify motor behavior into clinically meaningful clusters for predicting acute health events such as falls and delirium in older adults. I further advanced this work by introducing an unsupervised clustering framework tailored for multi-modal data. This approach jointly optimizes objectives for static and dynamic components, enabling the identification of high-risk individuals based on mobility and cognitive patterns, with potential applications in long-term care settings. ☐ Posterior collapse is a key challenge in representation-based time-series clustering, where latent variables become uninformative during training, causing the KL divergence to vanish and the model to ignore the latent space. This issue undermines clustering performance by preventing the model from learning meaningful data representations. To address this, I proposed an information-aware recovery mechanism that predicts collapse by monitoring mutual information and KL divergence, pauses training, and restores the model to a stable state with adjusted distributions. By semi-randomly reallocating data points, the model avoids retracing the same training path, improving the reliability and performance of clustering algorithms for unsupervised learning. ☐ This dissertation advances the application of deep learning in healthcare by integrating static and dynamic data for prediction, improving clustering methodologies, and addressing critical challenges in representation learning. These works aim for a foundation of more reliable and effective AI-driven healthcare analytics.University of Delaware, Department of Computer and Information SciencesPh.D
Teaching young multilingual learners: impacts of a professional learning programme on teachers’ practices and students’ language and literacy skills
No full textThis article was originally published in Journal of Multilingual and Multicultural Development. The version of record is available at: https://doi.org/10.1080/01434632.2025.2472880.
© 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.Using a randomised control trial, we evaluated the impact of a year-long professional learning (PL) programme for teachers designed to support their multilingual learners’ language and literacy skills. The PL included evidence-based instructional practices for literacy, a semi-structured model for collaboration, and a strengths-based approach in instruction. Participants included 39 kindergarten and first-grade teachers and 106 multilingual learners (MLs) from 13 schools in two districts in the Southeastern United States. Using a mixed modelling approach, we found significantly higher literacy growth on the Measures of Academic Progress (MAP) for MLs whose teachers participated in the PL. The PL also had a significant impact on teachers’ collaboration planning and processes with their students’ English as a Second Language (ESL) teachers. Effect sizes for the impact on teachers’ use of evidence-based instructional strategies and collaboration frequency were large but not statistically significant. The findings from this study show positive impacts of a teacher professional learning programme on teachers’ practices and young MLs’ literacy growth.This work was supported by Institute of Education Sciences [grant number R305A180336]