Unknown Samples Research Articles

Detection of low-level fentanyl concentrations in mixtures of cocaine, MDMA, methamphetamine, and caffeine via surface-enhanced Raman spectroscopy.

Surface-enhanced Raman spectroscopy (SERS) was utilized to measure low-level fentanyl concentrations mixed in common cutting agents, cocaine, 3,4-methylenedioxymethamphetamine (MDMA), methamphetamine, and caffeine. Mixtures were prepared with a fentanyl concentration range of 0-339 μM. Data was initially analyzed by plotting the area of a diagnostic peak (1026 cm-1) against concentration to generate a calibration model. This method was successful with fentanyl/MDMA samples (LOD 0.04 μM) but not for the other mixtures. A chemometric approach was then employed. The data was evaluated using principal component analysis (PCA), partial least squares (PLS1) regression, and linear discriminant analysis (LDA). The LDA model was used to classify samples into one of three designated concentration ranges, low = 0-0.4 mM, medium = 0.4-14 mM, or high >14 mM, with fentanyl concentrations correctly classified with greater than 85% accuracy. This model was then validated using a series of "blind" fentanyl mixtures and these unknown samples were assigned to the correct concentration range with an accuracy >95%. The PLS1 model failed to provide accurate quantitative assignments for the samples but did provide an accurate prediction for the presence or absence of fentanyl. The combination of the two models enabled accurate quantitative assignment of fentanyl in binary mixtures. This work establishes a proof of concept, indicating a larger sample size could generate a more accurate model. It demonstrates that samples, containing variable, low concentrations of fentanyl, can be accurately quantified, using SERS.

Open-world disaster information identification from multimodal social media

The application of multimodal deep learning for emergency response and recovery, specifically in disaster social media analysis, is of utmost importance. It is worth noting that in real-world scenarios, sudden disaster events may differ from the training data, which may require the multimodal network to predict them as unknown classes instead of misclassifying them to known ones. Previous studies have primarily focused on model accuracy in a closed environment and may not be able to directly detect unknown classes. Thus, we propose a novel multimodal model for categorizing social media related to disasters in an open-world environment. Our methodology entails utilizing pre-trained unimodal models as encoders for each modality and performing information fusion with a cross-attention module to obtain the joint representation. For open-world detection, we use a multitask classifier that encompasses both a closed-world and an open-world classifier. The closed-world classifier is trained on the original data to classify known classes, whereas the open-world classifier is used to determine whether the input belongs to a known class. Furthermore, we propose a sample generation strategy that models the distribution of unknown samples using known data, which allows the open-world classifier to identify unknown samples. Our experiments were conducted on two public datasets, namely CrisisMMD and MHII. According to the experimental results, the proposed method outperforms other baselines and approaches in crisis information classification.

Highly specific detection of ROR1 cancer biomarker with bipolar electrochemiluminescence.

An electrochemiluminescence (ECL) detection system is presented integrated with a bipolar electrode system for sensitive cancer diagnosis. In order to achieve the highest electrical conductivity and redox-active surface area, MXene was chosen as the material for the bipolar electrode. As part of the detection process, the anodic pole of the bipolar electrode was modified with the receptor tyrosine kinase like orphan receptor 1 (ROR1) antibody, followed by an immunoassay using the ROR1 antibody-modified Ag triangle that was identified as significantly enhancing ECL. We measured the ECL of luminol using the anode pole of BPE as an analytical signal in the presence of H2O2. Additionally, 3D-printed microchannels were used to fabricate the BPE system, to reduce the quantity of sample required. It has been shown that the present immunosensors are low-cost and sensitive in detecting types of cancer, with an extended linear range of 10 fg mL-1 to 1 µg mL-1 in the analysis of synthetic samples and achieving an accuracy of ~ 90% in diagnosing ten unknown real samples.

Atmospheric Pressure Photoionization with Halogen Anion Attachment for Mass Spectrometric Analysis of Hydrocarbons and Hydrocarbon-Based Polymers.

Aliphatic hydrocarbons and hydrocarbon-based synthetic polymers are of interest in many fields, but their characterization by mass spectrometric methods is generally limited due to their poor ionizability. Recently, atmospheric pressure photoionization (APPI), combined with halogen anion attachment in negative-ion mode, has drawn attention as a potential method for ionizing various polymers without extensive fragmentation or other unwanted side reactions. In this work, the applicability of halogen anion attachment with APPI was studied using several synthetic polymers, including polyethylene, polypropylene, polyisoprene, and polystyrene, as well as simple n-alkanes of various chain lengths. For hydrocarbon-based polymers, the method produced clear distributions of intact polymer adduct ions when different halogen anions were used. It was found that increasing the halogen anion size decreased ionization efficiency, particularly in the absence of π-bonds in the polymer structure. Testing with simple n-alkanes showed that only molecules containing fifty or more carbon atoms formed detectable halogen adducts, possibly due to the low gas-phase stabilities of the lighter n-alkane adduct ions. In conclusion, halogen anion attachment with negative-ion APPI appears to be a highly promising method for polymer analysis, providing structural data and clean polymer mass spectra with minimal fragmentation, which can be useful for the identification of unknown samples.

Microfluidics for foodborne bacteria analysis: Moving toward multiple technologies integration.

Food security related to bacterial pathogens has seriously threatened human life and caused public health problems. Most of the reported methods are targeted at known major pathogens commonly found in food samples, but to some extent, they have the disadvantage of lacking simplicity, speed, high throughput, and high sensitivity. Microfluidics has become a promising tool for foodborne bacteria analysis and addresses the above limitations. In this perspective, we briefly discussed the ongoing research and development in this field. We outline the major types of microfluidics, the strategies of target biorecognition, and signal amplification technologies in the microfluidic system for the foodborne bacteria analysis. We also proposed the future directions of microfluidics for foodborne bacterial analysis, which aims to integrate multiple technologies toward intelligent analysis with high selectivity and sensitivity for unknown samples, multiple bacterial detection, and simultaneous detection of multiple food pollutants.

On the Use of Image Analysis for Hematocrit Evaluation in Dried Blood Spots

Dried blood spots (DBSs) are formed by collecting a small sample of blood on specialized filter paper and allowing it to dry naturally. Various domains of life sciences and drug research extensively use DBSs as a sampling technique. The “Hematocrit (Ht) effect” affects assay bias, and several strategies have been put forth to deal with it, including the correction of quantified concentrations using an appropriate correction factor. The approach was previously applied, following the utilization of an image processing algorithm developed in Matlab® to derive a reliable equation correlating DBS areas to Ht% values. The present work looks more closely at the application of image analysis to the evaluation of Ht in DBS samples. Utilizing image analysis software, DBS samples with known Ht values were processed. Preparation of cards has followed a previously developed protocol for the appropriate formation of uniform area DBSs, irrespective of Ht. The resulting areas showed close resemblance to the respective theoretical areas calculated by applying the correlation equation. Following that, the equation was utilized to determine the Ht values for each sample, and a comprehensive comparison of measured versus calculated Ht was carried out using various statistical approaches for method comparison. The results demonstrated a strong correlation, suggesting the method’s viability in estimating Ht for unknown DBS samples.

Development of a potentiometric sensor for mercury (II) ion using cerium (IV) tinmolybdophosphate

ObjectivesThe primary objective of this report is to develop a heperopolyacid salt, Cerium (IV) tinmolybdophosphate (CeSnMoP), with distinctive attributes that significantly enhance its ion exchange capacity. Through deliberate adjustments in temperature, pH, and volume ratios, we have carefully prepared a range of CeSnMoP samples. One sample exhibiting an ion exchange capacity (IEC) of 5.06 ± 0.2 meq gm-1 has been identified for further extensive analysis. The second objective was to develop the potentiometric sensor by using the synthesised sample therefore it was transformed into an electrode incorporating PVC as binder material and validated as a potentiometric sensor for Mercury ions which can work as variable media.Data descriptionInstrumental analyses, such as XRD, IR, TGA, SEM and EDS, were used to elucidate the compound’s structural aspects. Distribution coefficient (Kd) studies highlighted the compound's pronounced selectivity towards Hg2+ ions. This catalyst was further utilized as an electro-active substance for detecting Hg2+ ions in an external solution. Epoxy resin played the role of a binder in the electrode formulations. The electrode, comprising a membrane with 50% exchanger material, demonstrated superior performance. This selected membrane exhibited a wide operational concentration range of 1 × 10–6 M – 1 × 10–1 M of Hg2+ ions for quantitative analysis of unknown samples of mercury ions. The lower detection limit for the calibration curve was recorded up to 2 × 10–8 M from 1 to 10–1 M. The electrode effectively sensed this metal ion within the pH range of 3.74–7.51 and exhibited a lifespan exceeding 8 months.

Non-target screening analysis of hazardous noxious substances using gas chromatography-quadrupole time-of-flight mass spectrometry

The forensic investigation of hazardous noxious substances (HNS) is paramount for an effective response to chemical spill emergencies and other accidents. Analyzing unknown emergency samples poses a challenge due to the limited availability of background information, making the selection of appropriate sample preparation and analytical methodologies difficult. The utilization of high-resolution mass spectrometers (HRMS) in screening both target and non-target substances proves instrumental in revealing hazardous substances that may be overlooked alongside the intended analytes. In this study, a gas chromatography-quadruple time-of-flight mass spectrometer (GC-QTOF-MS) was employed to identify numerous organic compounds in an indoor dust sample. The compounds detected encompassed normal alkanes, fatty acids (saturated and unsaturated), alcohols, phenols, sterols, drugs, polycyclic aromatic hydrocarbons (PAHs), pesticides, flame retardants (such as polybrominated biphenyl ethers, PBDEs), plasticizers (such as phthalates and phosphates), among others. Notably, concentrations of n-alkanes, fatty acids, and phthalates were relatively high, while PAHs and pesticides were present at trace levels. The application of GC-QTOF-MS provides a swift and confirmative approach for analyzing target, suspect, and non-target compounds in both routine and emergency scenarios. This methodology proves invaluable in enhancing our capability to comprehensively assess and address chemical incidents, ensuring a more thorough and accurate response.

Animal family discrimination from hair using ATR-FTIR and machine learning methods for applications in illegal wildlife trafficking.

Wildlife forensics plays a pivotal role in the combating illegal trafficking, supporting biodiversity conservation, and aiding in the identification of animals in wildlife. Animal hair, often found in trafficking crimes, serves as vital biological evidence that can provide significant information for animal identification. This study proposes a novel method integrating machine learning classifiers with Fourier transform infrared (FTIR) spectroscopy in attenuated total reflectance (ATR) mode to enhance the effectiveness of animal identification in wildlife forensic casework. Additionally, compound microscopy has also been utilized as a preliminary tool to perform morphological analysis of hair samples from four animal families, including Bovidae, Cervidae, Elephantidae, and Felidae. Further, chemical profiling through spectral data revealed significant overlapping peaks between family Bovidae and Cervidae. The classification experiment provides the random forest (RF) classifier as the most effective for family discrimination model. This research offers valuable insights for wildlife forensics by improving the identification accuracy of unknown hair samples, thus enhancing the overall effectiveness in forensic investigations.

Dynamic model-based intelligent fault diagnosis method for fault detection in a rod fastening rotor

A complete fault sample database is of great significance for the intelligent fault diagnosis method of rod fastening rotor. However, the lack of fault samples makes the fault diagnosis results unbelievable. To solve this issue, the dynamic model-based intelligent fault diagnosis method is established for a rod fastening rotor, and the fault sample database is enriched by numerical simulations. First, the lumped parameter model of the rod fastening rotor system is constructed and further updated using Euclidean Distance between measurement and numerical simulation of the intact system. Second, mathematical models of various fault types are incorporate into the updated model to obtain numerical simulation fault samples. Thirdly, the utilization of numerical simulation fault samples is severed as training data to the artificial intelligence (AI) models and the unknown measurement test samples will be finally classified. In this paper, Support Vector Machine, Random Forest, Bayesian Network and Decision Tree are selected as the typical AI models. Subsequently, the feasibility of classification is validated by the test bench of the rod fastening rotor system, and the problem of insufficient fault samples can be solved.

Rapid on-site multi-indicator detection of Polygonatum cyrtonema Hua by handheld near-infrared spectroscopy with wavelength selection

The rapid, accurate, and on-site quantitative evaluation of the 3 components (total polysaccharide, total flavonoid, and total saponin) of Polygonatum cyrtonema Hua (PCH) is of great significance to ensure the value of its medicine and food, for this purpose, a handheld near-infrared (NIR) spectrometer was used. The raw NIR spectra were preprocessed by seven methods and their combinations, then three kinds of wavelength selection algorithms, the competitive adaptive reweighted sampling (CARS), random frog (RF) sampling, and Monte Carlo uninformative variable elimination (MCUVE) were applied, and the characteristic wavelengths for total polysaccharide, total saponin, and total flavonoid in PCH were obtained, respectively. Subsequently, the partial least squares (PLS) regression models were developed. The results showed that the combination of first derivative and standard normal variate was the optimal pretreatment method, and CARS had the highest performance of the wavelength selection for total polysaccharide and total flavonoid, while RF was the highest for total saponin. With these selected wavelengths, 3 PLS prediction models were built, and their performances were validated with unknown samples, the results showed that the determination coefficient of the prediction set (R2P) of total polysaccharide, total saponin, and total flavonoid were 0.980, 0.948, and 0.942, respectively, and the corresponding root mean square error of prediction set (RMSEP) were 0.162, 0.142, and 0.052. Furthermore, the residual prediction deviations (RPDs) of the three components were all larger than 3, demonstrating the prediction performance of the models was high. Therefore, the three active functional components in PCH could be rapidly determined by a handheld NIR spectrometer, which would be beneficial for ensuring the quality of PCH.

Optically Pure Co(III) Complex Absorbed by Electrochemiluminescence-Active Covalent Organic Framework as an Enantioselective Recognition Platform to Give Opposite Responses Toward Amino Alcohol Enantiomers.

Although covalent organic frameworks (COFs) accompanied by electrochemiluminescence (ECL) behavior have been introduced in recent years, they are still rarely applied for ECL-based enantioselective sensing, especially giving high recognition efficiency. In the current study, an achiral ionic COF comprised of the pyridinium unit is synthesized in the linkage of the carbon-nitrogen cation bond through the Zincke reaction. Interestingly, the synthesized ionic COF can generate clear ECL owing to the presence of electroactive species. Then, the ECL-active achiral COF is employed to absorb the chiral Co(III) complex for enantioselective sensing. As a result, the developed ECL sensor displays discriminative responses toward amino alcohol enantiomers. When the chiral Co(III) complex with (R)-configuration is used, the examined (S)-amino alcohols result in ECL enhancement, whereas (R)-amino alcohols lead to ECL quenching. The maximum ECL intensity ratio between (S)- and (R)-amino alcohols is up to 47.7. In addition, the recognition mechanism is investigated in detail. Finally, a good linear relation between enantiomeric composition and ECL intensity is developed and appropriate for the accurate analysis of the enantiomeric purities of unknown samples. In short, we believe that this study constructs an effective strategy to combine the respective advantages of COFs and ECL for high-efficiency enantioselective sensing.

“Blue-red-purple” multicolored lateral flow immunoassay for simultaneous detection of GM crops utilizing RPA and CRISPR/Cas12a

Advanced multiplexed testing techniques should be designed and developed to ensure an accurate and reliable evaluation for unknown samples. In this study, an efficient platform coupled with the “Blue-Red-Purple” strategy based on recombinant polymerase amplification (RPA), CRISPR/Cas12a and lateral flow strip was established, which could realize the dual-target detection of CP4-EPSPS and Cry1Ab/Ac in genetically modified crops. The lateral flow immunoassay was developed using different colored microspheres to label the antibodies to realize the visualization of results and avoid cross-reactions. The proposed method exhibits high specificity, sensitivity and stability. The visual detection limits of standard plasmids and real samples reached 10 copies/μL and 0.5 %, respectively, which could be stored at 4 °C for 12 months with high detection ability. Moreover, the entire detection process could be completed within 50 min without any complex instruments or professional operators. These findings indicated that a sensitive, specific, rapid and accurate method was established for on-site detection of GM crops.

The Influence of Brand Image And Brand Love On Consumer Satisfaction Which Is Moderate By Word of Mouth At PT. POS Indonesia

This research aims to analyze the influence of brand image and brand love on PT consumer satisfaction. Pos Indonesia, as well as to test the moderating role of word of mouth in this relationship. This research was conducted on consumers of PT services. Indonesian post. The population in this study is unknown, so the researcher uses a reference from Hair et.al (2010) which states the minimum number of unknown samples is 100-200, so the sample in this study is 200. The sampling technique uses simple random sampling. Sampling was carried out by distributing questionnaires to consumers who came to the POS Indonesia office by providing a Google form link which was distributed to respondents. Partial Least Square (PLS) model analysis was used in this research. The results of this research are that brand image influences consumer satisfaction, brand love influences consumer satisfaction, word of mouth moderates brand image on consumer satisfaction and word of mouth moderates brand love on consumer satisfaction.

ELISA-R: an R-based method for robust ELISA data analysis.

Enzyme-linked immunosorbent assay (ELISA) is a technique to detect the presence of an antigen or antibody in a sample. ELISA is a simple and cost-effective method that has been used for evaluating vaccine efficacy by detecting the presence of antibodies against viral/bacterial antigens and diagnosis of disease stages. Traditional ELISA data analysis utilizes a standard curve of known analyte, and the concentration of the unknown sample is determined by comparing its observed optical density against the standard curve. However, in the case of vaccine research for complicated bacteria such as Mycobacterium tuberculosis (Mtb), there is no prior information regarding the antigen against which high-affinity antibodies are generated and therefore plotting a standard curve is not feasible. Consequently, the analysis of ELISA data in this instance is based on a comparison between vaccinated and unvaccinated groups. However, to the best of our knowledge, no robust data analysis method exists for "non-standard curve" ELISA. In this paper, we provide a straightforward R-based ELISA data analysis method with open access that incorporates end-point titer determination and curve-fitting models. Our modified method allows for direct measurement data input from the instrument, cleaning and arranging the dataset in the required format, and preparing the final report with calculations while leaving the raw data file unchanged. As an illustration of our method, we provide an example from our published data in which we successfully used our method to compare anti-Mtb antibodies in vaccinated vs non-vaccinated mice.

Pattern recognition-based aptasensor array for discrimination of matrix effect

Nucleic acid aptamers have attracted considerable attention in recent years, particularly in the fields of disease diagnosis and treatment and biosensing. However, complex matrix effects have severely hindered their progress towards practical application. In this study, the novel strategy of a pattern recognition-based aptasensor array was introduced for target recognition based on the predictable selectivity capability of aptamers by circumventing the challenge of the interference of the matrix effect. As one proof of concept, we chose lactoferrin as the target protein, three selected aptamers with differential affinity as its recognition probes, eleven matrix samples of milk powder as the pattern discrimination model, and AuNPs color as the response signals. In the AuNPs colorimetric arrays, the lactoferrin-added milk powder matrix induced different degrees of protection on the surface of AuNPs particles due to different degrees of binding of lactoferrin to the three aptamer sequences and therefore exhibited differentiated red-to-blue color change along with salt-induced aggregation behaviors. The difference in color responses exhibited with and without lactoferrin addition was considered as the fingerprint pattern of the lactoferrin in each milk powder matrix, followed by the supervised machine learning analysis of linear discriminant analysis (LDA) for better orthogonality to complete the identification and prediction of the unknown samples. By this strategy, eleven kinds of milk powder at the added concentration of lactoferrin (50 nmol/L) presented a distinct response pattern and have been identified and classified with accuracies of 100 % via LDA patterns (three aptamers × eleven milk powder matrices × five replicates) with three canonical factors (91.2 %, 8.58 %, and 0.22 %). Furthermore, the accuracy of 22 unknown samples was 100 %, indicating the achievement in differentiating between different milk powder matrices and the identification of each matrix. The proposed aptasensor array complements the traditional “lock-and-key” single aptasensor and opens a new direction for developing sensitive sensing array systems circumventing complex matrix effects.

A Convolutional Neural Network- Based Deep Learning To Detect Reticulocytes From Human Peripheral Blood

Machine learning approaches are rapidly augmenting, and in some cases, replacing the conventional methods in biomedical data analysis; to reduce time, cost, biases, and the need for sophisticated analytical platforms. Hence, significant interest has been compounded in the integration of automated image analysis for various clinical applications, such as the detection of infected or inflamed wounds, bone fractures or for the purpose of disease diagnosis – such as Plasmodium parasites or circulating tumour cells in blood. Here, we report the development of a Convolutional Neural Network (CNN)-based method on CPU to distinguish and count immature human red blood cells known as reticulocytes from blood smears. Reticulocytes represent a heterogeneous and relatively small percentage of cells in peripheral blood, and contain residual RNA in complex with proteins which generates thread-like patterns when stained with New Methylene Blue (NMB) dye. We used more than 200 NMB-stained images from leukocyte-depleted blood to train and optimize the model for immature reticulocytes (stained positive with NMB, intensity and pattern of which depends on the developmental stage of the reticulocyte) and mature RBCs (no staining with NMB). The training performance evaluation metrics demonstrated a mean average precision (mAP50) of 0.88, a precision of 0.83, a recall of 0.88, and an F1 score of 0.87. Our model was able to successfully count reticulocytes with accuracy more than 90% from unknown samples which were subsequently cross-verified through microscopy and counting. Given the importance of reticulocyte dynamics in blood and its clinical relevance, the newly developed model will find important easy to adopt biomedical applications that can be achieved on a simple PC.

Exploring the utility of complementary separations in liquid chromatography

An alternative strategy is explored for the separation of samples by liquid chromatography (LC). Unlike traditional approaches that aim to resolve all components in a given sample within a single LC separation, the proposed strategy uses two or more distinct separations carried out with a different gradient program and/or using different separation chemistries i.e., a different set of mobile and stationary phase. This set of complementary incomplete separations (CIS) is selected such that each component is at least fully resolved once, meaning the most critical pairs of each individual separation can be left unseparated. This allows for a significant time saving per separation. To investigate whether such an approach can lead to overall shorter analysis times than is possible with the fastest single-run gradient separation, a comprehensive in-silico study covering a statistically significant number of samples is undertaken. The investigation shows that, for the presently considered sample sets and chemistries, CIS has a substantially higher probability, about two times greater for the simplest samples considered in this work and as much as 30 times greater for more complex samples, to fully resolve an unknown sample compared to a single gradient separation. Comparing separation speeds, the CIS approach can achieve complete sample resolution on average approximately four times faster than a single separation. Our findings thus demonstrate the potential of CIS in enhancing separation efficiency and offer insights regarding their use for solving analytical challenges.

Lag Plot: A Novel Method to Determine Viable Bacterial Cell Number via a Single OD Measurement.

Bactericidal activity is a valuable parameter which is considered and measured for antimicrobial compounds. The available standard protocol to evaluate bactericidal activity is based on the direct colony count. Colony counting requires serial dilution, plating, overnight incubation, and direct counting, which is time-and labor-intensive. In regard to eliminate direct plate count, novel techniques were developed based on the real-time growth monitoring which can come with some limitations and drawbacks. These drawbacks encourage us to develop a novel technique with simple procedure to determine viable bacterial cell count. In this procedure, real-time growth monitoring is not required. In fact, after an incubation time, the number of viable bacteria can be determined with a single OD measurement and through an equation. In this regard, four standard bacterial strains, including Staphylococcus aureus (ATCC 25923), Escherichia coli (ATCC 25922), Pseudomonas aeruginosa (ATCC 27853), and Bacillus subtilis (ATCC 23857), were cultured with descending inoculum densities (1.5 × 107 to 15CFU/mL) and growth curves were drawn. As expected, growth in the samples with lower inoculum densities recorded with longer lag phase. Also, a direct relation was observed between the recorded turbidities and initial cell counts. A logarithmic curve (lag plot) was obtained by plotting the OD, after 12-18h incubation, against initial cell counts. In all examined strains, R2 was calculated in the range of 0.96-0.99 which is acceptable value for coefficient of determination. Equation corresponding to the lag plot was obtained and called lag equation. This equation is applicable to calculate the number of viable cells in unknown samples simply by inoculation, incubation, and single OD measurement. Developed technique can be introduced as a simple substitution for labor- and time-intensive direct colony counting.

Advances in Nickel Hydroxide: Structures and Modern Applications (review)

This review article provides an overview of research conducted over the past few decades on nickel hydroxide, a crucial material in both physics and chemistry with notable engineering applications, particularly in batteries. It begins by outlining the structures of the two known polymorphs, α-Ni(OH)2 and β-Ni(OH)2. The article also explores various types of disorder commonly found in nickel hydroxide, such as hydration, stacking faults, mechanical stresses, and the incorporation of ionic impurities. Related materials, including intercalated α-derivatives and basic nickel salts, are also discussed. The review goes on to summarize several methods for synthesizing nickel hydroxide, such as chemical and electrochemical precipitation, sol–gel synthesis, chemical aging, hydrothermal and solvothermal synthesis, electrochemical oxidation, microwave-assisted synthesis, and sonochemical methods. Finally, the known physical properties of nickel hydroxide—magnetic, vibrational, optical, electrical, and mechanical—are examined. The concluding section offers a summary of the potentially valuable properties of these materials and techniques for identifying and characterizing unknown nickel hydroxide-based samples.