National Standards Institute Research Articles

Electrically‐Reconfigurable Extremely‐High Density Physical Unclonable Cryptographic Keys Based on Aurivillius Ferroelectrics

AbstractIn the era of information, emerging hardware security primitives, i.e., hardware random number generators and physically unclonable functions, hold profound promise for the establishment of exceptionally secure network information systems. Nonetheless, these potential solutions are constrained by inherent drawbacks, including the need for additional error correction circuits or algorithms, heightened susceptibility to environmental interference, and limited data density. This paper demonstrates the extremely high‐density, physical‐unclonable cryptographic keys by harnessing stochastically ferroelectric domain polarizations in Aurivillius ferroelectric material CaBi2Nb2O9 (CBNO). Ferroelectric polarization of CBNO nanodomains can serve as a robust source of physical unclonable entropy. Through the application of high‐speed voltage pulses, the inherent randomness of ferroelectric polarizations can be enhanced, thereby yielding cryptographic keys characterized by remarkable uniformity, uniqueness, and negligible environmental sensitivity. More importantly, the voltage pulse operations facilitate the configurability of the ferroelectric cryptographic keys with no correlation. An unprecedented data density of 435 Gbit µm−2 is therefore achievable with a miniaturized CBNO of less than 1 um2. Notably, the reconfigured keys successfully pass the National Institute of Standards and Technology random number tests without requiring additional post‐processing steps. Emanating from the inherent attributes of the material, these high‐density ferroelectric keys confer intrinsic advantages to information security, evincing substantial resistance to duplication or cloning.

A GAN-based anomaly detector using multi-feature fusion and selection

In numerous applications, abnormal samples are hard to collect, limiting the use of well-established supervised learning methods. GAN-based models which trained in an unsupervised and single feature set manner have been proposed by simultaneously considering the reconstruction error and the latent space deviation between normal samples and abnormal samples. However, the ability to capture the input distribution of each feature set is limited. Hence, we propose an unsupervised and multi-feature model, Wave-GANomaly, trained only on normal samples to learn the distribution of these normal samples. The model predicts whether a given sample is normal or not by its deviation from the distribution of normal samples. Wave-GANomaly fuses and selects from the wave-based features extracted by the WaveBlock module and the convolution-based features. The WaveBlock has proven to efficiently improve the performance on image classification, object detection, and segmentation tasks. As a result, Wave-GANomaly achieves the best average area under the curve (AUC) on the Canadian Institute for Advanced Research (CIFAR)-10 dataset (94.3%) and on the Modified National Institute of Standards and Technology (MNIST) dataset (91.0%) when compared to existing state-of-the-art anomaly detectors such as GANomaly, Skip-GANomaly, and the skip-attention generative adversarial network (SAGAN). We further verify our method by the self-curated real-world dataset, the result show that our method is better than GANomaly which only use single feature set for training the model.

Organic-Inorganic Hybrid Synaptic Transistors: Methyl-Silsesquioxanes-Based Electric Double Layer for Enhanced Synaptic Functionality and CMOS Compatibility.

Electrical double-layer (EDL) synaptic transistors based on organic materials exhibit low thermal and chemical stability and are thus incompatible with complementary metal oxide semiconductor (CMOS) processes involving high-temperature operations. This paper proposes organic-inorganic hybrid synaptic transistors using methyl silsesquioxane (MSQ) as the electrolyte. MSQ, derived from the combination of inorganic silsesquioxanes and the organic methyl (-CH3) group, exhibits exceptional thermal and chemical stability, thus ensuring compatibility with CMOS processes. We fabricated Al/MSQ electrolyte/Pt capacitors, exhibiting a substantial capacitance of 1.89 µF/cm2 at 10 Hz. MSQ-based EDL synaptic transistors demonstrated various synaptic behaviors, such as excitatory post-synaptic current, paired-pulse facilitation, signal pass filtering, and spike-number-dependent plasticity. Additionally, we validated synaptic functions such as information storage and synapse weight adjustment, simulating brain synaptic operations through potentiation and depression. Notably, these synaptic operations demonstrated stability over five continuous operation cycles. Lastly, we trained a multi-layer artificial deep neural network (DNN) using a handwritten Modified National Institute of Standards and Technology image dataset. The DNN achieved an impressive recognition rate of 92.28%. The prepared MSQ-based EDL synaptic transistors, with excellent thermal/chemical stability, synaptic functionality, and compatibility with CMOS processes, harbor tremendous potential as materials for next-generation artificial synapse components.

Rapid screening and identification of targeted and non-targeted illegal added drugs in functional foods by MRSIT-HRMS based on NIST screening database

The last few decades have witnessed the increasing consumption of functional foods, leading to the expansion of the worldwide market. However, the illegal addition drugs in functional foods remains incessant despite repeated prohibition, making it a key focus of strict crackdowns by regulatory authorities. Effective analytical tools and procedures are desperately needed to rapidly screen and identify illegally added drugs in a large number of samples, given the growing amount and diversity of these substances in functional foods. The MRSIT-HRMS (Multiple Sample Rapid Introduction combined with High Resolution Mass Spectrometry) without chromatographic separation, after direct sampling, utilizes NIST software (National Institute of Standards and Technology) matching with a home-built library to target identification and non-targeted screen of illegal additives. When applied to 50 batches of suspicious samples, the targeted method detected illegal added drugs in 41 batches of samples, while the non-targeted method screened a new phosphodiesterase-5 (PDE-5) inhibitor type structural derivative. The positive results obtained by the targeted method were consistent with LC-MS/MS (QQQ). The novel MRSIT-HRMS with a limit of quantification (LOD) of 1 μg/mL achieved 100 % correct identification for all 50 batches of actual samples, demonstrating its potential as a highly promising and powerful tool for fast screening of illegally added drugs in functional food, especially when compared to traditional LC-MS/MS methods. This is essential for ensuring drug safety and public health.

Element Regulation and Dimensional Engineering Co-Optimization of Perovskite Memristors for Synaptic Plasticity Applications.

Capitalizing on rapid carrier migration characteristics and outstanding photoelectric conversion performance, halide perovskite memristors demonstrate an exceptional resistive switching performance. However, they have consistently faced constraints due to material stability issues. This study systematically employs elemental modulation and dimension engineering to effectively control perovskite memristors with different dimensions and A-site elements. Compared to pure 3D and 2D perovskites, the quasi-2D perovskite memristor, specifically BA0.15MA0.85PbI3, is identified as the optimal choice through observations of resistive switching (HRS current < 10-5 A, ON/OFF ratio > 103, endurance cycles > 1000, and retention time > 104 s) and synaptic plasticity characteristics. Subsequently, a comprehensive investigation into various synaptic plasticity aspects, including paired-pulse facilitation (PPF), spike-variability-dependent plasticity (SVDP), spike-rate-dependent plasticity (SRDP), and spike-timing-dependent plasticity (STDP), is conducted. Practical applications, such as memory-forgetting-memory and recognition of the Modified National Institute of Standards and Technology (MNIST) database handwritten data set (accuracy rate reaching 94.8%), are explored and successfully realized. This article provides good theoretical guidance for synaptic-like simulation in perovskite memristors.

Cyber Security; Etiology and Importance

Background: Cyber Security is to protect online data and software from cyber threats. These cyberattacks are typically intended to gain access to, change, or delete sensitive information; extort money from users; or disrupt regular corporate activities. It is difficult to keep up a regular follow up with new technologies, so it is necessary to keep the important data safe from cyber threats. There are many types of cyber threats, malware, ransomware, social engineering, phishing etc. To prevent cyber-attacks one can, use password manager tools like LastPass and others. People also use two factor authentication for double security on their accounts. Methods: Boards such as the National Institute of Standards and Technology (NIST) are developing frameworks to assist firms in understanding their security risks, improving cybersecurity procedures, and preventing cyber assaults. The fight against cybercrimes and attack, organizations needed a strong base there are 5 types of cyber securities: Critical Infrastructure Security, application security, network security, cloud security and (IoT) Security. In the modern time the US is highly based on computers and on different software, so it is really important for the US to be more conscious about the security as they get many threats almost every day for hacking their data and accounts. Results and Conclusion: Nowadays, even small businesses rarely recover their loss from the cyber-attacks and many back-off from continuing their businesses after being target of hackers. The first cybercrime attack was recorded in 1988 by a graduate student. Now that large companies and even small businesses are aware of cyber-attacks, they try their best to take every precaution to prevent hacking with double security and password manager tools.

Quantum random number generator based on LED

Quantum random number generators (QRNGs) produce random numbers based on the intrinsic probabilistic nature of quantum mechanics, making them true random number generators. In this paper, we design and fabricate an embedded QRNG that produces random numbers based on fluctuations of spontaneous emission and absorption in a light-emitting diode. To achieve a robust and reliable QRNG, we compare some usual post-processing methods and select the finite impulse response method for a real-time device. This device could pass the National Institute of Standards and Technology tests, the generation rate is 1 Mbit/s, and the randomness of the output data is invariant in time.

A lightweight hardware implementation of CRYSTALS-Kyber

The security of cryptographic algorithms based on integer factorization and discrete logarithm will be threatened by quantum computers in future. Since December 2016, the National Institute of Standards and Technology (NIST) has begun to solicit post-quantum cryptographic (PQC) algorithms worldwide. CRYSTALS-Kyber was selected as the standard of PQC algorithm after 3 rounds of evaluation. Meanwhile considering the large resource consumption of current implementation, this paper presents a lightweight architecture for ASICs and its implementation on FPGAs for prototyping. In this implementation, a novel compact modular multiplication unit (MMU) and compression/decompression module is proposed to save hardware resources. We put forward a specially optimized schoolbook polynomial multiplication (SPM) instead of number theoretic transform (NTT) core for polynomial multiplication, which can reduce about 74% SLICE cost. We also use signed number representation to save memory resources. In addition, we optimize the hardware implementation of the Hash module, which cuts off about 48% of FF consumption by register reuse technology. Our design can be implemented on Kintex-7 (XC7K325T-2FFG900I) FPGA for prototyping, which occupations of 4777/4993 LUTs, 2661/2765 FFs, 1395/1452 SLICEs, 2.5/2.5 BRAMs, and 0/0 DSP respective of client/server side. The maximum clock frequency can reach at 244 ​MHz. As far as we know, our design consumes the least resources compared with other existing designs, which is very friendly to resource-constrained devices.

Using Passive Microrheology to Measure the Evolution of the Rheological Properties of NIST mAb Formulations during Adsorption to the Air-Water Interface.

The development of novel protein-based therapeutics, such as monoclonal antibodies (mAbs), is often limited due to challenges associated with maintaining the stability of these formulations during manufacturing, storage, and clinical administration. An undesirable consequence of the instability of protein therapeutics is the formation of protein particles. MAbs can adsorb to interfaces and have the potential to undergo partial unfolding as well as to form viscoelastic gels. Further, the viscoelastic properties may be correlated with their aggregation potential. In this work, a passive microrheology technique was used to correlate the evolution of surface adsorption with the evolution of surface rheology of the National Institute of Standards and Technology (NIST) mAb reference material (NIST mAb) and interface-induced subvisible protein particle formation. The evolution of the surface adsorption and interfacial shear rheological properties of the NIST mAb was recorded in four formulation conditions: two different buffers (histidine vs phosphate-buffered saline) and two different pHs (6.0 and 7.6). Our results together demonstrate the existence of multiple stages for both surface adsorption and surface rheology, characterized by an induction period that appears to be purely viscous, followed by a sharp increase in protein molecules at the interface when the film rheology is viscoelastic and ultimately a slowdown in the surface adsorption that corresponds to the formation of solid-like or glassy films at the interface. When the transitions between the different stages occurred, they were dependent on the buffer/pH of the formulations. The onset of these transitions can also be correlated to the number of protein particles formed at the interface. Finally, the addition of polysorbate 80, an FDA-approved surfactant used to mitigate protein particle formation, led to the interface being surfactant-dominated, and the resulting interface remained purely viscous.

Ferroelectric 2D SnS2 Analog Synaptic FET.

In this study, the development and characterization of 2D ferroelectric field-effect transistor (2D FeFET) devices are presented, utilizing nanoscale ferroelectric HfZrO2 (HZO) and 2D semiconductors. The fabricated device demonstrated multi-level data storage capabilities. It successfully emulated essential biological characteristics, including excitatory/inhibitory postsynaptic currents (EPSC/IPSC), Pair-Pulse Facilitation (PPF), and Spike-Timing Dependent Plasticity (STDP). Extensive endurance tests ensured robust stability (107 switching cycles, 105 s (extrapolated to 10 years)), excellent linearity, and high Gmax /Gmin ratio (>105 ), all of which are essential for realizing multi-level data states (>7-bit operation). Beyond mimicking synaptic functionalities, the device achieved a pattern recognition accuracy of ≈94% on the Modified National Institute of Standards and Technology (MNIST) handwritten dataset when incorporated into a neural network, demonstrating its potential as an effective component in neuromorphic systems. The successful implementation of the 2D FeFET device paves the way for the development of high-efficiency, ultralow-power neuromorphic hardware which is in sub-femtojoule (48 aJ/spike) and fast response (1 µs), which is 104 folds faster than human synapse (≈10ms). The results of the research underline the potential of nanoscale ferroelectric and 2D materials in building the next generation of artificial intelligence technologies.

Multifunctional HfAlO thin film: Ferroelectric tunnel junction and resistive random access memory.

This study presents findings indicating that the ferroelectric tunnel junction (FTJ) or resistive random-access memory (RRAM) in one cell can be intentionally selected depending on the application. The HfAlO film annealed at 700 °C shows stable FTJ characteristics and can be converted into RRAM by forming a conductive filament inside the same cell, that is, the process of intentionally forming a conductive filament is the result of defect generation and redistribution, and applying compliance current prior to a hard breakdown event of the dielectric film enables subsequent RRAM operation. The converted RRAM demonstrated good memory performance. Through current-voltage fitting, it was confirmed that the two resistance states of the FTJ and RRAM had different transport mechanisms. In the RRAM, the 1/f noise power of the high-resistance state (HRS) was about ten times higher than that of the low-resistance state (LRS). This is because the noise components increase due to the additional current paths in the HRS. The 1/f noise power according to resistance states in the FTJ was exactly the opposite result from the case of the RRAM. This is because the noise component due to the Poole-Frenkel emission is added to the noise component due to the tunneling current in the LRS. In addition, we confirmed the potentiation and depression characteristics of the two devices and further evaluated the accuracy of pattern recognition through a simulation by considering a dataset from the Modified National Institute of Standards and Technology.

The Image Classification Algorithm Was Implemented on The MNIST Data Set

In the current era of rapid development of science and technology, the recognition and classification of digital images is the key to solving many problems, such as the application of license plate recognition, document digitization, and remote sensing image surface classification. Based on the MNIST handwritten numerical data set collected by the National Institute of Standards and Technology (NIST), this report uses Python language and PyTorch programming framework to construct a convolutional neural network (CNN) structure and practice and experience the image classification of handwritten digits in the MNIST data set.

A COMPARATIVE REVIEW OF DATA ENCRYPTION METHODS IN THE USA AND EUROPE

Data encryption is a critical aspect of modern information security, and understanding the approaches taken by different regions is vital for a comprehensive analysis. In the United States and Europe, data encryption methods vary in implementation, legal frameworks, and overall priorities. In the United States, encryption methods are primarily governed by a combination of federal laws and industry standards. The National Institute of Standards and Technology (NIST) plays a central role in recommending cryptographic standards, while the Department of Commerce oversees export controls on encryption technology. The focus in the U.S. is on balancing national security needs with individual privacy rights. The tension between law enforcement's desire for access to encrypted data for criminal investigations and the right to privacy has sparked debates and legal battles. On the other hand, Europe has taken a more privacy-centric approach to data protection. The General Data Protection Regulation (GDPR) is a cornerstone in the European Union's efforts to safeguard individual privacy rights. GDPR mandates the use of encryption to protect personal data, and failure to implement adequate measures can result in hefty fines. European countries also emphasize the importance of end-to-end encryption in communication services to ensure confidentiality. Both regions prioritize encryption, but their approaches reflect different values and legal philosophies. The U.S. tends to navigate a delicate balance between national security and individual rights, while Europe places a stronger emphasis on the protection of personal data as a fundamental right. In terms of technological implementation, the encryption algorithms adopted in both regions are often aligned with global standards. Advanced Encryption Standard (AES) is widely accepted and implemented in various sectors. However, the choice of key management and the level of regulatory oversight differ, contributing to the nuanced landscape of data protection. In conclusion, a comparative review of data encryption methods in the USA and Europe reveals the complex interplay between security, privacy, and legal frameworks. Understanding these differences is crucial for multinational organizations and individuals navigating the intricate landscape of global data protection.&#x0D; Keywords: Data, Encryption, USA, Europe, Review.

Advancing Hardware Security: A Review and Novel Design of Configurable Arbiter PUF with DCM-Induced Metastability for Enhanced Resource Efficiency and Unpredictability

As the Internet of Things (IoT) and Blockchain technologies continue to assert their dominance in the technical landscape, the demand to enhance security measures becomes foremost. In this context, Physical Unclonable Functions (PUFs) are widely used hardware security primitives that can be used to solve a wide range of security issues. To support hardware security solutions, this paper presents an extensive overview and analysis of the existing Physical Unclonable Functions (PUFs) used as True Random Number Generators (TRNGs). Recognizing the shortcomings of current PUF designs, we propose a configurable Arbiter PUF design employing Digital Clock Manager (DCM)-induced metastability as an entropy source, presenting a robust solution for evolving hardware security. To mitigate the adverse consequences of metastability, the proposed Arbiter PUF includes a Carry Chain primitive with four Flip-Flop clones. Acknowledging the constantly evolving IoT and Blockchain environment, the suggested configurable Arbiter PUF is made to satisfy the highest security standards. By exploiting the inherent variations in FPGA technology, we aim to reduce system resource and area consumption, aligning with the efficiency criteria of modern applications. The system's performance is additionally enhanced by an on-chip post-processing based on DSP. Simulation results demonstrate successful implementation on a Xilinx Basys-3 FPGA board, offering a scalable and efficient solution. The generated sequences of the proposed PUF undergo rigorous testing, including National Institute of Standards and Technology (NIST) statistical tests for uniqueness, reliability, and randomness. This holistic approach aims to improve the PUF's performance and security.&#x0D;

Governing Ethical Gaps in Distributed AI Development

Good business practice often leads companies to subdivide into separate functional entities for operational efficiency and specialization. However, these kinds of divisions can generate significant ethical and perhaps even regulatory gaps when they occur in AI companies. In particular, one natural division for an AI company is into separate entities responsible for model development, testing, and cybersecurity (to maintain and protect data). In this paper, we argue that this division can lead to some ethical responsibilities always being “someone else’s job.” For concreteness, we consider the US National Institute of Standards and Technology’s AI Risk Management Framework (NIST AI RMF) as a guide to ethical obligations in a corporate context. We show that a common division of labor in AI development and deployment can lead to specific obligations for which no entity is responsible, even though they apply to the effort as a whole. We propose “Join Accountability Agreements”, a mechanism to ensure that ethical obligations do not slip through the cracks because of the way an effort is structured. We thus aim to highlight the significance of comprehensive examinations of and adaptable strategies for our ethical obligations when developing AI systems in a distributed manner.

Multi-kilowatt cw laser power measurement comparison between national standards

We present here the first comparison between National Metrology Institutes of high accuracy continuous wave optical power measurements in the kilowatt regime. The National Institute of Standards and Technology (NIST) performed measurements with a power meter relying on photon momentum. The Physikalisch-Technische Bundesanstalt (PTB) performed measurements with a modified off-the-shelf thermal power meter. The non-absorbing photon momentum measurement approach permits the two power meters to measure the same laser beam optical path simultaneously, resulting in a direct comparison of the meters supported by an optical system to accommodate differences in instrument settling times. The results show agreement within the expanded uncertainties for each instrument. NIST and PTB illustrate a degree of equivalence of 0.49% with an expanded uncertainty of 1.37% (k = 2) for an average result across all power levels.

A Survey about Post Quantum Cryptography Methods

Cryptography is an art of hiding the significant data or information with some other codes. It is a practice and study of securing information and communication. Thus, cryptography prevents third party intervention over the data communication. The cryptography technology transforms the data into some other form to enhance security and robustness against the attacks. The thrust of enhancing the security among data transfer has been emerged ever since the need of Artificial Intelligence field came into a market. Therefore, modern way of computing cryptographic algorithm came into practice such as AES, 3DES, RSA, Diffie-Hellman and ECC. These public-key encryption techniques now in use are based on challenging discrete logarithms for elliptic curves and complex factorization. However, those two difficult problems can be effectively solved with the help of sufficient large-scale quantum computer. The Post Quantum Cryptography (PQC) aims to deal with an attacker who has a large-scale quantum computer. Therefore, it is essential to build a robust and secure cryptography algorithm against most vulnerable pre-quantum cryptography methods. That is called ‘Post Quantum Cryptography’. Therefore, the present crypto system needs to propose encryption key and signature size is very large.in addition to careful prediction of encryption/decryption time and amount of traffic over the communication wire is required. The post-quantum cryptography (PQC) article discusses different families of post-quantum cryptosystems, analyses the current status of the National Institute of Standards and Technology (NIST) post-quantum cryptography standardisation process, and looks at the difficulties faced by the PQC community.

In vitro bioaccessibility round robin testing for arsenic and lead in standard reference materials and soil samples.

In this study, we assessed the suitability of using a standard reference material (SRM) other than National Institute of Standards and Technology (NIST) 2710a or NIST 2711a in USEPA Method 1340 to determine arsenic (As) and lead (Pb) in vitro bioaccessibility (IVBA) and the capabilities of Canadian-based laboratories to perform the method. Five laboratories participated in an initial round robin study and analyzed NIST 2710a, NIST 2711a, BGS119, and Enviromat SS-2. Intra- and inter-laboratory variability were generally acceptable with percentage relative standard deviations (RSD) of less than 20%. The mean total As and Pb concentrations obtained for BGS119 (332 and 936 mg/kg, respectively) and the mean IVBA values (As = 14.3% and Pb = 78.1%) suggested it may be a suitable and acceptable SRM, whereas the concentration of As in Enviromat SS-2 as received (3.2 mg/kg) was deemed too low. Ten soil samples from sites with varying land use were analyzed in a follow-up round robin study using the modified IVBA method that included BGS119 as SRM. The concentrations of As and Pb in the IVBA extracts reported by the participating laboratories were comparable. The mean As IVBA values for the field-collected samples ranged from 0.1% to 56.4%; for Pb, they ranged from 7.0% to 121%. The lowest IVBA values were measured in mine site samples; the highest values were associated with smelter-affected soils. The low IVBA values correlated with high iron content. Intra- and interlaboratory reproducibility were acceptable (RSD < 30%). Based on the findings of the study, laboratories can use the modified method to provide reproducible and comparable As and Pb IVBA data. The use of BGS119 as an alternative SRM to assess contaminated sites in the province of British Columbia for regulatory purposes is recommended, as it is representative of As and Pb concentrations in contaminated soils in British Columbia. Integr Environ Assess Manag 2024;20:1486-1495. © 2024 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Handwritten digit recognition based on deep learning techniques

The identification of handwritten digits in images recognition and machine learning is a prominent research area. In order to create a handwritten digit recognition model for this investigation, deep learning is introduced. The proposed approach integrates Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and EfficientNetB0, three separate deep learning models. Specifically, the CNN model utilizes pooling layers and data augmentation techniques to enhance its classification ability, the RNN model takes advantage of its ability to process sequential data, and the EfficientNetB0 model benefits from a deeper and more complex network structure. These models are trained and evaluated using the Modified National Institute of Standards and Technology (MNIST) dataset. The experimental results demonstrate the efficacy of the proposed approach: the CNN model attains a remarkable accuracy of 98.9% on the test set, thereby showcasing its exceptional classification performance. Similarly, the RNN model achieves an accuracy of 96.7%, underscoring its suitability for analyzing sequential data. Furthermore, the EfficientNetB0 model attains an accuracy of 98.1%, thereby elucidating the benefits of the deeper network architecture. The models constructed in this study have significant real-world implications, such as improved object recognition systems, medical diagnostics, and autonomous driving. The EfficientNetB0 model produced an accuracy of 98.1% with its complex network architecture when applied to recognise handwritten digits.

Comparative Analysis of Phytocompound Variations in Leaves, Bark and Roots of Allspice (Pimenta Dioica) Collections in Tanzania

Allspice, scientifically known as Pimenta dioica, holds potential as a natural source of beneficial compounds that have been historically used to address various human health concerns. The aim of this research was to explore differences in the compounds found in parts of Allspice (i.e., the leaves, bark, and roots). Petroleum ether, dichloromethane, and methanol were used to extract the substances from each part; the resulting crude extracts were then analyzed using gas chromatography mass spectrometry. To interpret the obtained data, the National Institute of Standards and Technology database was referred to for a spectra analysis. The findings indicated that the leaves contained 81 phytocompounds, bark had 18 types, and roots exhibited 12 varieties. Prominent phytocompounds found in the leaves were eugenol in 72.24% – 73.91% of the total compounds detected. Bark was primarily composed of 1,4-Methanoazulene, decahydro-4,8,8-trimethyl-9-methylene-, [1S-(1. alpha.,3a. beta.,4. alpha.,8a. beta.)] in 74.35% – 84.24%; while roots contained γ-sitosterol at an 86.08% concentration level. In terms of solvent performance, methanol exhibited high efficiency on leaves, while dichloromethane demonstrated optimal results on bark and roots. The findings confirm significant variations in phytocompound composition in different parts of Allspice and underscores the importance of considering specific types of phytocompounds, as well as extraction techniques to achieve valuable outcomes.