SearcharxivSearch

arXiv · 2507.18299

Synthesis of nanoparticles from carboxymethyl cellulose using one-pot hydrothermal carbonization for Drug Entrapment Studies

Abstract

Porous nanomaterials have recently attracted a lot of attention due to various properties and potential applications. In this study, carbon nanoparticles (CNPs) were synthesized by the one-pot hydrothermal carbonization (HTC) using carboxymethyl cellulose (CMC). Urea was used as the nitrogen source for carbonization. The presence of urea in CMC solution for carbonization resulted in CNPsu reduction in the diameter of particles from 4 micrometer to 1 micrometer. Activation process at high temperature for both the above samples resulted in nanoparticles with diameter of 51 nm and 31 nm, respectively. The positive effect of presence urea and its activation generated different functional groups including C-N, N-H, and C -(triple bond)- N with increasing aromatic rings that probably may help entrapment of drugs into them. On the other hand, activation CNPsu (ACNPsu) has the most aromatic rings with the lowest hydroxyl groups with 84.66% carbon and 12.29% oxygen in its structures. ACNPs, and ACNPsu exhibited a type I isotherm indicating microporous materials with a high surface area about 552.9 m2/g and 351.01 m2/g, respectively. The high surface area was characteristic of activated carbons with their high adsorption capacity. Thus, the synthesized materials were characterized using SEM, TEM, DLS, BET, FTIR, HNMR, and TGA techniques. Finally, the encapsulation of clindamycin drug (CD) with positive charge in different types of NPs with negative charge was investigated for drug delivery in biomedical engineering applications.

Explore related subjects

Keep this discovery

BibTeXRIS

Mohaddeseh Sharifi, S. Hajir Bahrami. 2025-07-24. Synthesis of nanoparticles from carboxymethyl cellulose using one-pot hydrothermal carbonization for Drug Entrapment Studies. https://arxiv.org/abs/2507.18299

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Sequence-Informed Geometric Evaluation of RNA 3D Structures

Computational RNA structure pipelines generate many candidate conformations for the same sequence. Reliable evaluation therefore requires more than recognising plausible geometry, it requires determining whether that geometry is compatible with the sequence. We introduce SIRGE, a sequence-informed geometric evaluator that conditions structural representations on nucleotide embeddings from a pretrained RNA language model. Early results show that SIRGE outperforms established evaluators in Kendall--$\tau$ alignment, Top-1 selection, and Top-3 ranking. Controlled comparisons further show that sequence conditioning corrects errors made by an otherwise matched geometric model and improves target-level rank structure. These findings provide initial evidence that pretrained sequence representations supply ranking information that complements geometric reasoning.

q-bio.BM

PocketVE: Stable and Property-Guided Structure-Based Drug Design with Variance-Exploding Diffusion

Protein-conditioned 3D molecule generation is a central challenge in structure-based drug design, requiring a balance between pocket compatibility, molecular properties, and physical geometry. We propose \textbf{PocketVE}, a protein-pocket-conditioned variance-exploding (VE) diffusion framework that couples stable coordinate denoising with inference-time property guidance. Specifically, PocketVE combines an EDM-style training and sampling setup for 3D denoising, classifier-free guidance for multi-property steering without external property classifiers, and adaptive protein perturbation as a training-time pocket regularizer. Evaluated on CrossDocked2020 under the GenBench3D protocol, PocketVE improves Valid$_{3\text{D}}$ from 58.6 to 80.6 and reduces strain energy from 457.4 to 127.9 relative to its TAGMol architectural baseline, while retaining competitive docking and molecular-property scores under moderate guidance. A guidance-scale study shows that moderate guidance gives a favorable balance between target-related objectives and geometric quality, whereas stronger guidance can degrade geometry and distributional fidelity. Pocket-permutation and PoseCheck diagnostics further support pocket-specific spatial compatibility with reduced steric conflicts. Overall, the results suggest that geometric stability and inference-time property guidance should be considered as coupled design objectives.

q-bio.BM

Predicting directional flexibility in proteins

Predicting protein dynamics is a long-standing problem in computational structural biology. Often, protein function critically depends on local directed motions, such as hinge movements, catalytic loop rearrangements and domain reorientations, which can be characterized by directional flexibility and correlated structural motions of the protein backbone. While Molecular Dynamics (MD) simulations provide an established but often prohibitively expensive approach, recent deep generative models aim to reduce this cost by directly predicting conformational ensembles, emulating MD. However, due to their large size and the need to generate several states until the derived dynamical properties converge, these models remain expensive. In this work, we propose BackFlip-2: a fast SE(3)-equivariant graph neural network trained to directly predict dynamical descriptors, such as directional backbone flexibility and pairwise dynamic correlations, from an equilibrium structure. In a series of experiments, we show that our model matches the accuracy of substantially larger ensemble generation models while being orders of magnitude faster, and demonstrate that the proposed equivariant architecture is especially well-suited for capturing anisotropic motions in proteins. BackFlip-2 model weights, training and inference code are available at https://github.com/graeter-group/backflip.

q-bio.BM