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Au Nanomatryoshkas as Efficient Near-Infrared Photothermal Transducers for Cancer Treatment: Benchmarking against Nanoshells

ACS nano, 2014-06, Vol.8 (6), p.6372-6381

2014

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Adsorption of Cr(VI) on activated rice husk carbon and activated alumina

Bioresource technology, 2004-02, Vol.91 (3), p.305-307

2004

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Calibration of Boundary-Type Earth Pressure Cells with Sand

Geotechnical testing journal, 2024-07, Vol.47 (4), p.863-878

2024

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Clay calcination technology: state-of-the-art review by the RILEM TC 282-CCL

Materials and structures, 2022, Vol.55 (1), Article 3

2022

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Determination of the intrinsic kinetics of CO2 gas hydrate formation using in situ particle size analysis

Chemical engineering science, 2005-02, Vol.60 (3), p.695-709

2005

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Determination of the intrinsic rate of ethane gas hydrate decomposition

Chemical engineering science, 2000-11, Vol.55 (21), p.4869-4883

2000

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Understanding the compositional control on electrical, mechanical, optical, and physical properties of inorganic glasses with interpretable machine learning

Acta materialia, 2023-01, Vol.242, p.118439, Article 118439

2023

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The effect of authentic project‐based learning on attitudes and career aspirations in STEM

Journal of research in science teaching, 2019-01, Vol.56 (1), p.3-23

2019

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Measurement variability of calibration factor of earth pressure cells with different contact materials

Measurement : journal of the International Measurement Confederation, 2024-01, Vol.224, p.113934, Article 113934

2024

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Predicting Young's modulus of oxide glasses with sparse datasets using machine learning

Journal of non-crystalline solids, 2019-11, Vol.524, p.119643, Article 119643

2019

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Determination of the activation energy and intrinsic rate constant of methane gas hydrate decomposition

Canadian journal of chemical engineering, 2001-02, Vol.79 (1), p.143-147

2001

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Predicting oxide glass properties with low complexity neural network and physical and chemical descriptors

Journal of non-crystalline solids, 2023-09, Vol.616, p.122488, Article 122488

2023

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Measuring and modelling the rate of decomposition of gas hydrates formed from mixtures of methane and ethane

Chemical engineering science, 2001-08, Vol.56 (16), p.4715-4724

2001

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Discovering symbolic laws directly from trajectories with hamiltonian graph neural networks

Machine learning: science and technology, 2024-09, Vol.5 (3), p.35049

2024

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Determination of the intrinsic rate constant and activation energy of CO2 gas hydrate decomposition using in-situ particle size analysis

Chemical engineering science, 2004-07, Vol.59 (14), p.2983-2993

2004

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Interpreting the optical properties of oxide glasses with machine learning and Shapely additive explanations

Journal of the American Ceramic Society, 2022-06, Vol.105 (6), p.4046-4057

2022

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Glass hardness: Predicting composition and load effects via symbolic reasoning-informed machine learning

Acta materialia, 2023-08, Vol.255, p.119046, Article 119046

2023

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A Method To Predict Equilibrium Conditions of Gas Hydrate Formation in Porous Media

Industrial & engineering chemistry research, 1999-06, Vol.38 (6), p.2485-2490

1999

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A simple ballistic material model for soda-lime glass

International journal of impact engineering, 2009-03, Vol.36 (3), p.386-401

2009

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Deep learning aided rational design of oxide glasses

Materials horizons, 2020-07, Vol.7 (7), p.1819-1827

2020

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Imaging of low Z masked with high Z (Pb, U) materials using 14 MeV neutron

Journal of instrumentation, 2024-05, Vol.19 (5), p.P05022

2024

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Methane Hydrate: Melting and Memory

Annals of the New York Academy of Sciences, 2000-01, Vol.912 (1), p.474-482

RODGER, P. MARK

2000

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Scalable Gaussian processes for predicting the optical, physical, thermal, and mechanical properties of inorganic glasses with large datasets

Materials advances, 2021-01, Vol.2 (1), p.477-487

2021

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Probing the superconducting gap structure in the noncentrosymmetric topological superconductor ZrRuAs

Physical review. B, 2021-04, Vol.103 (14), Article 144516

2021

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Au Nanomatryoshkas as Efficient Near-Infrared Photothermal Transducers for Cancer Treatment: Benchmarking against Nanoshells

Adsorption of Cr(VI) on activated rice husk carbon and activated alumina

Report of RILEM TC 267-TRM phase 2: optimization and testing of the robustness of the R3 reactivity tests for supplementary cementitious materials

Calibration of Boundary-Type Earth Pressure Cells with Sand

Clay calcination technology: state-of-the-art review by the RILEM TC 282-CCL

Determination of the intrinsic kinetics of CO2 gas hydrate formation using in situ particle size analysis

Determination of the intrinsic rate of ethane gas hydrate decomposition

Understanding the compositional control on electrical, mechanical, optical, and physical properties of inorganic glasses with interpretable machine learning

The effect of authentic project‐based learning on attitudes and career aspirations in STEM

Measurement variability of calibration factor of earth pressure cells with different contact materials

Predicting Young's modulus of oxide glasses with sparse datasets using machine learning

Determination of the activation energy and intrinsic rate constant of methane gas hydrate decomposition

Predicting oxide glass properties with low complexity neural network and physical and chemical descriptors

Measuring and modelling the rate of decomposition of gas hydrates formed from mixtures of methane and ethane

Discovering symbolic laws directly from trajectories with hamiltonian graph neural networks

Determination of the intrinsic rate constant and activation energy of CO2 gas hydrate decomposition using in-situ particle size analysis

Interpreting the optical properties of oxide glasses with machine learning and Shapely additive explanations

Glass hardness: Predicting composition and load effects via symbolic reasoning-informed machine learning

A Method To Predict Equilibrium Conditions of Gas Hydrate Formation in Porous Media

A simple ballistic material model for soda-lime glass

Deep learning aided rational design of oxide glasses

Imaging of low Z masked with high Z (Pb, U) materials using 14 MeV neutron

Methane Hydrate: Melting and Memory

Scalable Gaussian processes for predicting the optical, physical, thermal, and mechanical properties of inorganic glasses with large datasets

Probing the superconducting gap structure in the noncentrosymmetric topological superconductor ZrRuAs

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